Reviewerly/exHarmony huggingface dataset:
Reactive Navigation Framework for Mobile Robots by Heuristically Evaluated Pre-sampled Trajectories

This paper describes and analyzes a reactive navigation framework for mobile robots in unknown environments. The approach does not rely on a global map and only considers the local occupancy in its robot-centered 3D grid structure. The proposed algorithm enables fast navigation by heuristic evaluations of pre-sampled trajectories on-the-fly. At each cycle, these paths are evaluated by a weighted cost function, based on heuristic features such as closeness to the goal, previously selected trajectories, and nearby obstacles. This paper introduces a systematic method to calculate a feasible pose on the selected trajectory, before sending it to the controller for the motion execution. Defining the structures in the framework and providing the implementation details, the paper also explains how to adjust its offline and online parameters. To demonstrate the versatility and adaptability of the algorithm in unknown environments, physics-based simulations on various maps are presented. Benchmark tests show the superior performance of the proposed algorithm over its previous iteration and another state-of-art method. The open-source implementation of the algorithm and the benchmark data can be found at https://github.com/RIVeR-Lab/tentabot.
Confirmatory Factor Analysis (CFA) Model for Testing Normality with the Weight Least Square (WLS) Estimation Method

To complete the analysis problem of the data which form ordinal scale, we used Structural Equation Modeling (SEM) or Lisrel. This research aims to evaluate the normality and covariant matrix estimation from the ordinal data which unknown the spreading form in each sample 100, 150, 200, and 300 by using Weighted Least Square (WLS). Ordinal data appeared from the spreading of lisrel aid and used matrix covariant aid. Path of diagram also used to test of the suitable model with WLS. The normally graphic of each sample test are the Kolmogorov-Smirnov test, Anderson-Darling test, and Ryan-Joiner test. Determination of model quality could be used based on three groups of the model, they are absolute model: and RMSEA, incremental model: GFI and AGFI, and parsimony model: PGFI. This research found the normality by using the WLS method in samples 100, 150, 200, and 300 by using Kolmogorov-Smirnov test, Anderson-Darling test, Ryan-Joiner test. According to the result, it could be found that for samples 100, 150, 200, and 300, the spread of the data was normal and the best test used the Kolmogorov-Smirnov test. This result shows that if the sample has a larger size, it means the sample has the best value in the test performed.
Prioritization of Supply Chain Reconfiguration Variables using Balanced Score Card and Analytic Network Process

Supply chain reconfiguration has emerged as a key variable that determines the capability of supply chain to adjust itself in wake of risks or dynamic business environment.However, supply chain reconfiguration is dependent on several variables that need to be prioritized to focus resources for improvement.The study reported in this paper proposes a multi-criteria decision approach to prioritize the variables associated with supply chain reconfiguration.The novelty of the proposed model lies in its capability to integrate the dimensions of balance scorecard and analytic network process.The proposed model is evaluated for a case supply chain from retail sector.
Modern telecommunication systems and messengers as the alternative didactic platforms after Covid-19 pandemic lockdown

Objectives: The article aims to discuss educational problems during the period of 2019-nCoV (SARS-CoV-2) pandemic lockdown in Russia and examines the peculiarities of innovative educational technologies within the modern educational paradigm in the context of teaching under a lockdown pressure. Special attention is paid to analyses of positive and negative aspects of lockdown distance learning, representing the concept of innovative pedagogy as a combined pedagogical approach that offers an alternative to existing teaching methods. The authors consider a lockdown educational experience to be a first step to a new era of educational technologies based on a principle of health safety and proper time management that could be achieved by application of online teaching methods. The authors describe the alternative didactic platforms such as Zoom and WhatsApp; define the term “distance learning” and the phenomenon designated by it. Special attention is paid to the consideration of a number of factors that hinder the introduction of innovative educational technologies and the reform of the educational system as a whole in the context of a lockdown. Methodology: In our research we applied several methods that suit the objectives of the work: Statistical Data collection; Observation; Analysis.
Trust or Usefulness? QR Code Payment Among Millennials in a Disrupted Market

The digitization trend has increased dramatically in the past decade.The development of the internet and the increasing use of smartphones worldwide indicate the starting of the industrial revolution 4.0, which is now moving into a society 5.0 era.This trend has affected almost all activities of human life, including marketing, where traditional transaction processes began to shift to the digital ones.Mobile payment using Quick Response (QR) code is one of the digital transaction options in Indonesia.This study aims to investigate the key factors that encourage millennials to use the QR code in the payment process.The study focuses on four variables: mobile usefulness, trust, attitude, and behavioral intention.Trust and usefulness are often the reasons people adopt new technologies, yet the question of which one is the most influential factor for millennials arises.To answer this question, researchers surveyed 165 Indonesian millennial respondents.The results show that usefulness has a direct positive effect on using QR codes in mobile payments.Meanwhile, trust has the largest indirect effect on using QR codes through the mediation of the attitude variable.
Comparative Analysis of Word Embeddings in Assessing Semantic Similarity of Complex Sentences

Semantic textual similarity is one of the open research challenges in the field of Natural Language Processing. Extensive research has been carried out in this field and near-perfect results are achieved by recent transformer-based models in existing benchmark datasets like the STS dataset and the SICK dataset. In this paper, we study the sentences in these datasets and analyze the sensitivity of various word embeddings with respect to the complexity of the sentences. In this article, we build a complex sentence dataset comprising of 50 sentence pairs with associated semantic similarity values provided by 15 human annotators. Readability analysis is performed to highlight the increase in complexity of the sentences in the existing benchmark datasets and those in the proposed dataset. Further, we perform a comparative analysis of the performance of various word embeddings and language models on the existing benchmark datasets and the proposed dataset. The results show the increase in complexity of the sentences has a significant impact on the performance of the embedding models resulting in a 10-20% decrease in Pearson’s and Spearman’s correlation.
Research on the Construction and Application of Breast Cancer-Specific Database System Based on Full Data Lifecycle

Relying on the Biomedical Big Data Center of West China Hospital, this paper makes an in-depth research on the construction method and application of breast cancer-specific database system based on full data lifecycle, including the establishment of data standards, data fusion and governance, multi-modal knowledge graph, data security sharing and value application of breast cancer-specific database. The research was developed by establishing the breast cancer master data and metadata standards, then collecting, mapping and governing the structured and unstructured clinical data, and parsing and processing the electronic medical records with NLP natural language processing method or other applicable methods, as well as constructing the breast cancer-specific database system to support the application of data in clinical practices, scientific research, and teaching in hospitals, giving full play to the value of medical big data of the Biomedical Big Data Center of West China Hospital.
A Novel Analog Circuit Fault Diagnosis Approach

Background: Correct classifying of analog circuit faults is helpful in the health management of the circuit. It is difficult to be implemented because of the lack of proper feature extraction methods and accurate fault diagnosis models. Objective: T-SNE based core components extraction method and PSO-ELM-based fault diagnosis model are presented to improve the diagnostic accuracy of analog circuit fault diagnosis. Methods: Firstly, circuit output signals are collected, and they are transformed to wavelet coefficients. Then, the high-dimensional wavelet coefficients are processed by t-SNE to generate lowdimensional core components as features. The Extreme Learning Machine (ELM) based diagnosing model is constructed by using the features, and the key parameters of ELM are optimized by using Particle Swarm Optimization (PSO) algorithm. Finally, the constructed PSO-ELM diagnosis model is employed to identify different analog circuit faults. Results: Leapfrog filter circuit and three-phase bridge circuit fault diagnosis experiments are implemented to demonstrate the proposed t-SNE based features extraction method and PSO-ELM based fault diagnosis model. Also, comparisons are performed to verify the high performance of proposed fault diagnosis methods. Conclusion: The proposed t-SNE based core components extraction method and PSO-ELM diagnosis model are effective to improve the fault diagnosis accuracy of the analog circuit.
Realizing Machine Learning’s Promise in Geoscience Remote Sensing

Machine learning and signal processing methods offer significant benefits to the geosciences, but realizing this potential will require closer engagement among different research communities.
Blockchain-based Identity and Access Management in Industrial IoT Systems

It’s been ten years since blockchain technology was created. This amalgam of cryptography and peer to peer application brings many innovations and securities services beyond financial services to regular information systems and offers new use cases for distributed applications. Industrials Internet of Things systems have issues with delivering identities to devices, controlling access or managing a vast amount of data. In this paper, we will go through the benefits a blockchain infrastructure can bring to the industrial Internet of Things (IoT) systems and present a new approach for a distributed Identity and Access Management (IAM) using blockchain technology as the communication layer. The proposed work in progress architecture is suited for consortium of companies sharing a fleet of IoT devices. The blockchain ensures the integrity and the non repudiability of instruction at destination to the IAM controllers.
Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks

We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved. We deploy heuristic algorithms and exhaustive ones to generate Bayesian networks among the monitored variables. The aim is to describe the relevant relationships between the variables, to discover and confirm the possible cause–effect relationships, to predict the fuel consumption dependent on the contextual conditions of traffic, and to enable an intervention analysis to be conducted on the variables so that our goals are achieved. We propose a validation technique using Bayesian networks based on Granger causality: it relies upon observations of the time series formed by successive values of the variables in time. We use the same method based on Granger causality to rank the Bayesian networks obtained as well. A comparison of the Bayesian networks discovered against the ground truth is proposed in a synthetic data set, specifically generated for this study: the results confirm the validity of the Bayesian networks that agree on most of the existing relationships.
Development of GAMIFICATION Model for Flipped Learning

Purpose: This study aims to develop a gamification model for flipped learning for the systematization and simplicity of gamified teaching and learning design for teachers and for learners’ enjoyable, self-directed learning during the pre- and post-classes. As the world has recently experienced a crisis in the form of the COVID-19 pan-demic, teaching and learning environments have changed a lot. Flipped learning can be used as an alternative strategy for improving educational effectiveness while considering the issue of learners’ safety in crisis situations. Flipped learning enables mastery learning and improves learning effects with a learning style that suits students and increases learner participation. Method: To verify the validity of the gamification model for flipped learning derived through FGI , the content validity and face validity were verified by a group of experts using a testing tool. The criteria established for selecting the group of experts were those who had a doctorate degree in a major field or had more than ten years of educational experience and who are currently teaching in universities or public educational sectors. The validity test criteria consisted of ‘validity,’ ‘utility,’ and ‘usefulness’ as a class design model, and ‘understanding’ and ‘value’ for the explanation , and they were each scored on a 5-point Likert scale. Results: The average value of the validity for each evaluation item was found to be within the range of 4.2 to 4.6, indicating that all the elements of the model were properly recognized. The calculations for each evaluation factor also show that the ratio(CVR) was distributed within the range of 0.75 to 1.00. A final model for flipped learning classes incorporating gamification was derived, validated, and verified. The four procedural elements of flipped learning using gamification are navigate(N), aim(A), build(B), and implement(I). Conclusion: A gamification model for flipped learning, consisting of navigate, aim, build, and implement(NABI) procedures, was developed and has been proven to be valid. A strategic approach was needed to improve the current state of flipped learning, and gamification, which can lead to the active participation of participants, was necessary. These attempts will serve as a significant opportunity to implement flipped learning more dynamically. It is also expected that gamification will be a catalyst for learners’ enjoyable, effective, memorable, sustainable and meaningful learning experiences inside and outside the classroom.
Syntactic Networks of Interlanguage Across L2 Modalities and Proficiency Levels

Over time, interlanguage studies have shifted from early qualitative to quantitative studies of specific linguistic structures. However, the focus of these studies is usually on one aspect of an interlanguage instead of the whole system. The ideal object of interlanguage research is a second language (L2) learner language system, for only in this way can the entire L2 learning process can be examined. As a self-organizing and self-regulated system, the panorama of interlanguage can be revealed objectively through a complex network approach. In this study, we construct eight interlanguage dependency syntactic networks of varying proficiency levels and modalities, and conduct a quantitative study of respective network parameters. We find that all syntactic networks of Chinese L2 learners (English native speakers) initially present scale-free and small-world properties. Additionally, there is no sudden syntactic emergence in interlanguage with different modalities. This suggests varying regularities in the development of a syntactic network between interlanguage and native language acquisition. Moreover, the first language plays an important role in L2 development. The network parameters (&amp;lt; k &amp;gt;), L, C, ND , and NC can differentiate interlanguage modalities, and five quantitative parameters, &amp;lt; k &amp;gt;, C, ND , γ′, and NC , can indicate L2 proficiency.
Machine Learning Techniques to Classify Breast Cancer

This chapter explores the working of three different machine learning algorithms to detect which one of them would give the best performance in one of the most crucial domains of health and medicine: breast cancer. Cancer is one of the major leading causes of death globally, and according to the World Health Organization, in the year 2012, 8.2 million deaths globally were caused by cancer, and it is further assumed that the death ratio is likely to rise up to 27 million by the end of 2030. The onset of breast cancer takes place by an uncontrolled division of the cells from breast tissue, which results in the formation of a mass known as a tumor. Breast cancer is an ubiquitous issue faced by people and doctors. Many systems and models have been prepared to classify these breast cells using various methods, the most common one being analyzing either a given dataset containing necessary information or analyzing mammographic images.
Research of Single Image Rain Removal Algorithm Based on LBP-CGAN Rain Generation Method

Rain has an undesirable negative effect on the clarity of the collected images. In situation where images are captured in rain, it can lead to a loss of information and disability in reflecting real images of the situation. Consequently, rain has become an obstacle in outdoor scientific research studies. The reason why images captured in rain are difficult to process is due to the indistinguishable interactions between the background features and rain textures. Since current image data are only processed with the CNN (convolutional neural network) model, a trained neural network to remove rain and obtain clear images, the resulted images are either insufficient or excessive from standard results. In order to achieve more ideal results of clearer images, series of additional methods are taken place. Firstly, the LBP (local binary pattern) method is used to extract the texture features of rain in the image. Then, the CGAN (conditional generative adversarial network) model is constructed to generate rain datasets according to the extracted rain characteristics. Finally, the existing clear images, rain datasets generated by CGAN, as well as the images with rain are used for convolution operation to remove rain from the images, and the average value of PSNR (peak signal to noise ratio) can reach 38.79 by using this algorithm. Moreover, a large number of experiments are done and have proven that this joint processing method is able to successfully and effectively generate clear images despite the rain.
A new approach for Cauchy noise removal

<abstract> In this paper, a new total generalized variational (TGV) model for restoring images with Cauchy noise is proposed, which contains a non-convex fidelity term and a TGV regularization term. In order to obtain a strictly convex model, we add an appropriate proximal term to the non-convex fidelity term. We prove that the solution of the proposed model exists and is unique. Due to the convexity of the proposed model and in order to get a convergent algorithm, we employ an alternating minimization algorithm to solve the proposed model. Finally, we demonstrate the performance of our scheme by numerical examples. Numerical results demonstrate that the proposed algorithm significantly outperforms some previous methods for Cauchy noise removal. </abstract>
Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021

This paper describes our systems that were submitted to the restricted translation task at WAT 2021.In this task, the systems are required to output translated sentences that contain all given word constraints.Our system combined input augmentation and constrained beam search algorithms.Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output.For both En→Ja and Ja→En, our systems obtained the best translation performances in both automatic and human evaluations.
An Incremental Acquisition Method for Web Forensics

In order to solve the problems of repeated acquisition, data redundancy and low efficiency in the process of website forensics, this paper proposes an incremental acquisition method orientecd to dynamic websites. This method realized the incremental collection on dynamically updated websites through acquiring and parsing web pages, URL deduplication, web page denoising, web page content extraction and hashing. Experiments show that the algorithm has relative high acquisition precision and recall rate, and can be combined with other data to perform effective digital forensics on dynamically updated real-time websites.
E-banking fraud detection: a short review

E-banking gives customers a lot of satisfaction in terms of getting a better service quality; it also gives banks a competitive advantage over the other players in the sector. However, the security of e-banking has received attention due to the fraudulent behaviour of fraudsters; the absence of adequate e-banking security has kept many people away from the service till today. In this paper, a review of the security challenges associated with e-banking has been presented. Equally, the challenges and characteristics of e-banking fraud have been mirrored. This paper also reviewed different types of fraud and attacks detection systems, as well as some preventive measures in place to secure e-banking services. The different techniques and models used for e-banking security were ranked in this study based on an expert opinion. From the results, Transaction Monitoring was identified as the most effective model while the worst models based on the respondent's opinions were Virtual Keyboards, Browser Protection, and Device Identification. This paper is organized into sections; the first section introduced the topic of interest in this paper while the second section presented the background of e-banking. The literature review was presented in the third section while a conclusion was presented in the last section of the paper.
Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces

Numerous invariant (or equivariant) neural networks have succeeded in handling the invariant data such as point clouds and graphs. However, a generalization theory for the neural networks has not been well developed, because several essential factors for the theory, such as network size and margin distribution, are not deeply connected to the invariance and equivariance. In this study, we develop a novel generalization error bound for invariant and equivariant deep neural networks. To describe the effect of invariance and equivariance on generalization, we develop a notion of a quotient feature space, which measures the effect of group actions for the properties. Our main result proves that the volume of quotient feature spaces can describe the generalization error. Furthermore, the bound shows that the invariance and equivariance significantly improves the leading term of the bound. We apply our result to a specific invariant and equivariant networks, such as DeepSets, and show that their generalization bound is considerably improved by \sqrt{n!}, where n! is the number of permutations. We also discuss the expressive power of invariant DNNs and show that they can achieve an optimal approximation rate. Moreover, we conducted experiments to support our results.