Reviewerly/exHarmony huggingface dataset:
Progress in self-correcting methods of projector nonlinearity for fringe projection profilometry

In fringe projection profilometry, the luminance nonlinearity of the projector has been recognized as one of the most crucial factors decreasing the measurement accuracy. It induces the ripple-like artifacts on the measured phase map. The self-adaptive correcting algorithms, i.e., self-correcting algorithms, allow us to suppress the effect of the projector nonlinearity without a prior calibration for the projector intensities or phase errors. This paper introduces the research progress in the self-correcting algorithms. Among them, the first algorithm is to determine a nonlinear curve representing the projector nonlinearity, directly from the captured fringe patterns, thus correcting the phase errors using this curve. The second one is to recognize and remove the nonlinearity-induced errors, directly from a calculated phase map. With the last one, error function coefficients are estimated from a couple of phase maps having different frequencies. Measurement results demonstrate these self-correcting algorithms to be effective in suppressing influences of the projector nonlinearity in the absence of any calibration information.
MODELING ANALYSIS WITH ADVANCED TECHNOLOGY IN ENGLISH ONLINELEARNING PLATFORM

With the development of Internet innovation, an ever-increasing number of individuals pick online stage to learn on the web. The rise of English web-based learning stage has introduced an influx of upsurge. So as to improve student's reaction capacities and appreciate great learning assets, the online conduct displaying examination was utilized to enormously improve the impact of English internet learning. Based on the current stage, the students' activity treats in the stage order conduct were gotten, to set up an appropriate web based taking in conduct assessment model from the points of view of students' inclinations and learning results. The foundation of learning conduct assessment model can give logical and sensible direction and help for students' learning conduct.
PROVENANCE AWARE AUDIT TRAIL FRAMEWORK FOR SAAS PROVIDERS EFFICIENCY ASSESSMENT IN CLOUD ENVIRONMENT

The dynamic features of cloud computing enlights on the importance of service selection and service delivery issues. Cloud service provider plays an optimistic role in delivering of services to the consumer as per their preferences. In this regard efficiency measures act as key factors for performance analysis of provider. In promoting of efficiency measures we undertake audit trail framework in service delivery issues. We propose a provenance aware approach for assessing providers deliverables in the context of SaaS at cloud environment. The base snapshot of providers data acquired from the provenance helps in delivering number of services offered to consumer and their accessibility features by the auditor to perform audit trails. Earlier provenance based audit trails contributed more on economic and policy monitoring issues. This audit trail framework recognizes the importance of performance measures on providers service delivery and their access.
Minimising the Churn Out of the Service by Using a Fairness Mechanism

The paper proposes an algorithm of bandwidth distribution, ensuring fairness to end-users in computer networks. The proposed algorithm divides users into satisfied and unsatisfied users. It provides fairness in terms of quality of experience (QoE) for satisfied users and quality of service (QoS) for unsatisfied users. In this paper, we present detailed comparisons relevant to service providers to show the advantages of the proposed algorithm over the popular max-min algorithm. Our algorithm is designed to provide service providers with a mechanism to minimize the number of end-user terminations of service, which is one of the most desired factors for service providers.
Reference Frame Identification and Distributed Control Strategies in Human-Robot Collaboration

The reference frame definition is a significant postulate for the robot motion planning process. Reference frame selection can be classified as allocentric, egocentric and route-centric. The identification of a reference frame affects the complexity of measuring and implementing the controlling variables. The work reports this topic by referring specifically at the human robot collaboration in a workstation. Accordingly, this work proposes a methodology for the evaluation of the reference frame in a human robot communication aimed at enabling, effective and safe distributed control strategies.
Design and Development of Ladder Climbing Robot

This paper presents the development of a ladder climbing robot prototype. The development of the prototype includes constructing the basic software and hardware for the robot. The robot controller is designed using Arduino Mega 2560 software implemented in Arduino IDE with C programming. The hardware development involves constructing the main chassis and climbing arm with servomotor, DC geared motor and motor driver. An algorithm to climb ladder is developed with application of Infrared (IR) sensor and ultrasonic sensors. Significantly, the robot prototype is tested on ladders of different rung spacing. In the future, a base with wheels will be constructed for the robot to carry load and move on ground with obstacle avoidance capability and side grippers will be constructed to improve climbing performance of the robot on vertical ladder; this project will contribute to future research on similar topics.
Graph Based Approach for Image Data Retrieval in Medical Application

Now-days most of the people are suffering in diabetic, retinopathy and glaucoma. It is non-invensive action and experimental study. In general manual images are manually seen by trained clinician in a time consume and not accurate result. This paper describes new technique such as max-flow graph base approach. Max-flow based techniques more accurate result than PCA and Hessian method. Initial get fundus image on DRIVE and STAIR database, show that the new graph base technique is effective and positive in relation to similar System.
A STEP FORWARD IN FRAUD DETECTION SYSTEM USING MACHINE LEARNING

Machine Learning is considered as one of the applications of Artificial Intelligence that can provide the systems an ability to automatically learn or we can acquire the knowledge itself without being manually programmed. The dream of every online store owner which can be an e-commerce website, online retailers etc. is to become successful and to have a well-recognized startup company or even a famous brand. And it is a proven fact that the more popular or the more famous your brand name is, the more frequently it is to come in eyes of the fraudsters which means it will have a high tendency to become a target of an online fraud. After gathering the information from the various organizations, it was brought into notice that the percentage of the total revenue loss by fraudulent transactions in the year 2018 was 1.80%, which was an increase of 0.28% from the previous year in which the percentage of fraudulent transactions was 1.58%. And it was also noticed that the average cost for each dollar which was lost in the fraud rose by $0.17 from the previous year and reached to $2.94 in the year 2018.A fraudulent transaction generally leads to chargeback which is the sum that is to be returned to the customer by the seller/merchant after a fraudulent transaction has taken place. In processing a chargeback, the merchant has to bear operational cost also which includes transaction fees, legal fees, currency conversions etc. So, the task is to build a model thats predictive but also minimizes the cost to the company not only by correctly flagging and identifying the fraudulent transaction but also to minimize the cost of wrong predictions.
PROMOTING ENGLISH SPEAKING SKILLS: LESSONS LEARNT FROM THE EFL CLASSROOMS CONTEXT

As they are candidate of school English teachers in Indonesian schools, graduates of English education programs in Indonesia are expected to demonstrate adequate understanding of English pedagogical knowledge and uses. Yet, it is widely acknowledged that during their language and teaching training, many of these teacher candidates often experience various challenges, particularly in the domain of English-speaking skills. This paper, therefore, sought to explore how teacher educators (TEs) attempted to deal with such challenges in their teaching praxis. Drawing on a qualitative research approach, data were collected from TEs working in a university in the north part of Indonesia who were selected purposively, and classroom observation field-notes. Findings revealed two major themes, designing classroom tasks and promoting group work, which highlights the need to create a fun and stimulating classroom atmosphere for the TEs to foster improvement in English speaking skills. Article visualizations:
4K or Not? - Automatic Image Resolution Assessment

Recent years have witnessed a growing popularity of 4K or ultra high definition (UHD) content. However, the acquisition, production, post-production, and distribution pipelines of such content often go through stages where the actual video resolution goes below 4K/UHD level and is then upscaled to 4K/UHD resolution at later stages. As a result, the claimed 4K content in the real world often drops below the intended 4K quality, while final consumers are not well informed about such quality degradation. Here, we present our recent research progress on automatic image resolution assessment methods that determine whether a given image has true 4K resolution or not. Specifically, we developed a largest of its kind database of more than 10,000 true and fake 4K/UHD images with ground-truth labels. We have also made some initial attempts on constructing edge feature, Fourier transform feature, and deep learning based methods for the classification task. We believe that the built database and the attempted methods will help accelerate the research progress on automatic image resolution assessment.
Functional Outlier Detection through Probabilistic Modelling

Functional data consist in the most typical case of one-dimensional curves that represent the evolution of some physical parameter of interest with time. However, the analysis of this kind of objects is far from being simple, and the possibility of treating contaminated data is a classical problem that can arise in this framework as frequently as in the multivariate one. This justifies the development a new functional outlier detection technique based on functional measures capable of capturing the the outlyingness in the magnitude and shape sense that is presented in this paper.
On Indeterminate Strings Matching.

Given two indeterminate equal-length strings p and t with a set of characters per position in both strings, we obtain a determinate string p_w from p and a determinate string t_w from t by choosing one character per position. Then, we say that p and t match when p_w and t_w match for some choice of the characters. While the most standard notion of a match for determinate strings is that they are simply identical, in certain applications it is more appropriate to use other definitions, with the prime examples being parameterized matching, order-preserving matching, and the recently introduced Cartesian tree matching. We provide a systematic study of the complexity of string matching for indeterminate equal-length strings, for different notions of matching. We use n to denote the length of both strings, and r to be an upper-bound on the number of uncertain characters per position. First, we provide the first polynomial time algorithm for the Cartesian tree version that runs in deterministic 𝒪(nlog² n) and expected 𝒪(nlog nlog log n) time using 𝒪(nlog n) space, for constant r. Second, we establish NP-hardness of the order-preserving version for r=2, thus solving a question explicitly stated by Henriques et al. [CPM 2018], who showed hardness for r=3. Third, we establish NP-hardness of the parameterized version for r=2. As both parameterized and order-preserving indeterminate matching reduce to the standard determinate matching for r=1, this provides a complete classification for these three variants.
Faster Dynamic Range Mode

In the dynamic range mode problem, we are given a sequence $a$ of length bounded by $N$ and asked to support element insertion, deletion, and queries for the most frequent element of a contiguous subsequence of $a$. In this work, we devise a deterministic data structure that handles each operation in worst-case $\tilde{O}(N^{0.655994})$ time, thus breaking the $O(N^{2/3})$ per-operation time barrier for this problem. The data structure is achieved by combining the ideas in Williams and Xu (SODA 2020) for batch range mode with a novel data structure variant of the Min-Plus product.
On the Sample Complexity of Learning Sum-Product Networks

Sum-Product Networks (SPNs) can be regarded as a form of deep graphical models that compactly represent deeply factored and mixed distributions. An SPN is a rooted directed acyclic graph (DAG) consisting of a set of leaves (corresponding to base distributions), a set of sum nodes (which represent mixtures of their children distributions) and a set of product nodes (representing the products of its children distributions). In this work, we initiate the study of the sample complexity of PAC-learning the set of distributions that correspond to SPNs. We show that the sample complexity of learning tree structured SPNs with the usual type of leaves (i.e., Gaussian or discrete) grows at most linearly (up to logarithmic factors) with the number of parameters of the SPN. More specifically, we show that the class of distributions that corresponds to tree structured Gaussian SPNs with $k$ mixing weights and $e$ ($d$-dimensional Gaussian) leaves can be learned within Total Variation error $\epsilon$ using at most $\widetilde{O}(\frac{ed^2+k}{\epsilon^2})$ samples. A similar result holds for tree structured SPNs with discrete leaves. We obtain the upper bounds based on the recently proposed notion of distribution compression schemes. More specifically, we show that if a (base) class of distributions $\mathcal{F}$ admits an compression, then the class of tree structured SPNs with leaves from $\mathcal{F}$ also admits an efficient compression.
Sea Ionizing Radiation Dynamic Measurement Method Based on Discrete Radiation Intensity Method

Zhu, W.; Hou, C.; Zhang, Q., and Liang, J., 2020. Sea ionizing radiation dynamic measurement method based on discrete radiation intensity method. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 835–838. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the stability of ionizing magnetic field at sea, it is necessary to optimize the measurement of ionizing radiation intensity at sea. A dynamic measurement method of ionizing radiation at sea based on discrete radiation intensity method is proposed. The parameter estimation model of dynamic measurement of ionizing radiation at sea is constructed, and the dynamic fitting of dynamic measurement data of ionizing radiation intensity at sea is carried out by using power spectral density estimation method. The distribution model of discrete radiation intensity characteristic quantity at sea is constructed, and the measurement error compensation correction is carried out combined with the discrete radiation intensity dynamic error compensation method to improve the accuracy of marine ionizing radiation intensity dynamic measurement. The generalized cross-correlation parameter estimation method of discrete radiation intensity characteristic decomposition is used to realize the dynamic measurement of offshore ionizing radiation. The simulation results show that the dynamic measurement of ionizing radiation at sea is accurate, the correction ability of discrete radiation intensity is strong, and the measurement error is small.
Probability theory and public-key cryptography

In this short note, we address a common misconception at the interface of probability theory and public-key cryptography.
Research on camera calibration method based on coplanar points

In order to achieve the camera calibration, the calculation process of the camera’s internal and external parameters was obtained through the established camera calibration model. Based on the coplanar points, the camera calibration model was simplified. With distortion model and Levenberg-Marquardt algorithm, the system calibration’s accuracy was improved. The experimental results showed that the calibration error was smaller and the error data was more concentrated, which realized the accurate calibration of the camera.
Research on Evaluation Model of Oral English Teaching Quality based on Cloud Computing

Aiming at the problem of low evaluation accuracy of the existing evaluation model of oral English teaching quality, this paper proposes an evaluation model of oral English teaching quality based on cloud computing. The evaluation indicator is divided into expert's evaluation indicator, student's evaluation indicator and teacher's self-evaluation indicator and a comprehensive evaluation indicator set is obtained. The cloud weight in cloud computing model is described by three digital eigenvalues: weight value, entropy value and hyper-entropy value, and the corresponding evaluation cloud model is set up. Based on the cloud computing principle, the cloud evaluation model of oral English teaching quality is constructed. The experimental results show that the model can effectively evaluate the quality of oral English teaching. The selected evaluation indicators are highly representative and the evaluation results are accurate.
Cross-Sentence Transformations in Text Simplification

Current approaches to Text Simplification focus on simplifying sentences individually. However, certain simplification transformations span beyond single sentences (e.g. joining and re-ordering sentences). In this paper, we motivate the need for modelling the simplification task at the document level, and assess the performance of sequence-to-sequence neural models in this setup. We analyse parallel original-simplified documents created by professional editors and show that there are frequent rewriting transformations that are not restricted to sentence boundaries. We also propose strategies to automatically evaluate the performance of a simplification model on these cross-sentence transformations. Our experiments show the inability of standard sequence-to-sequence neural models to learn these transformations, and suggest directions towards document-level simplification.
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods.

Artificial Intelligence (AI) has become an integral part of domains such as security, finance, healthcare, medicine, and criminal justice. Explaining the decisions of AI systems in human terms is a key challenge--due to the high complexity of the model, as well as the potential implications on human interests, rights, and lives . While Explainable AI is an emerging field of research, there is no consensus on the definition, quantification, and formalization of explainability. In fact, the quantification of explainability is an open challenge. In our previous work, we incorporated domain knowledge for better explainability, however, we were unable to quantify the extent of explainability. In this work, we (1) briefly analyze the definitions of explainability from the perspective of different disciplines (e.g., psychology, social science), properties of explanation, explanation methods, and human-friendly explanations; and (2) propose and formulate an approach to quantify the extent of explainability. Our experimental result suggests a reasonable and model-agnostic way to quantify explainability
Probing the Natural Language Inference Task with Automated Reasoning Tools

The Natural Language Inference (NLI) task is an important task in modern NLP, as it asks a broad question to which many other tasks may be reducible: Given a pair of sentences, does the first entail the second? Although the state-of-the-art on current benchmark datasets for NLI are deep learning-based, it is worthwhile to use other techniques to examine the logical structure of the NLI task. We do so by testing how well a machine-oriented controlled natural language (Attempto Controlled English) can be used to parse NLI sentences, and how well automated theorem provers can reason over the resulting formulae. To improve performance, we develop a set of syntactic and semantic transformation rules. We report their performance, and discuss implications for NLI and logic-based NLP.
Exponential Fourier Moment-Based CBIR System: A Comparative Study

Content-based image retrieval (CBIR) is a technique for browsing, searching, and retrieving images from a large database of image collections. The availability of large amount of image collections necessitates powerful algorithms for image retrieval. CBIR system extracts image information known as features that are used to retrieve relevant images from image database that best match with query image. A moment-based content-based image retrieval system is explored in this paper. Exponential Fourier moment-based CBIR system, improved exponential Fourier moment-based CBIR system, and accurate and fast exponential Fourier-based CBIR system are developed. Comparative analysis is performed on three moment-based CBIR systems in terms of average precision and retrieval time for the two benchmark databases GT face and COIL-100. Among the three moment-based CBIR systems, it is observed that accurate and fast exponential Fourier-based CBIR system delivers good results.
Comparison of Different Metaheuristic Algorithms for Multilevel Non-local Means 2D Histogram Thresholding Segmentation

Multilevel image segmentation technique segregates an image into disjoint regions and has application in many real-world problems like object recognition, boundary estimation of motion systems, image compression, etc. Conventional image segmentation does not consider the spatial correlation of image’s pixels and lack in providing better post-filtering efficiency. This paper performs an analysis of results obtained from different metaheuristic algorithms using an efficient technique of 2D histogram multilevel thresholding based on non-local means filter and Renyi entropy. Further, this study aims to compare newly proposed whale optimization algorithm with some prominent algorithms in recent past and some conventional metaheuristic algorithms to achieve an efficient image segmentation.
Verification-Guided Tree Search

Monte-Carlo Tree Search (MCTS) algorithms have been adopted by the artificial intelligence planning community for decades due to their ability to reason over long time horizons while providing guarantees on the convergence of the solution policy. In recent years, such algorithms have been modernized through the integration of Convolutional Neural Networks (CNNs) as part of the state evaluation process. However, both traditional and modern MCTS algorithms suffer from poor performance when the underlying reward signal of the environment is sparse. In this paper, we propose a verification-guided tree search solution which incorporates a reward shaping function within modern MCTS implementations. This function leverages the mathematical representation of the underlying objective by leveraging techniques from formal verification.