Reviewerly/exHarmony huggingface dataset:
Shape reconstruction for deflectometry based on chebyshev polynomials and iteratively reweighted least squares regression

Abstract Deflectometry is a technique for measuring the slope data of specular surfaces, and shape reconstruction is the final process based on the measured slopes. Modal methods reconstruct surfaces with expansion polynomials. The coefficients of each polynomial mode are calculated by linear equations composed of the gradient of the polynomials and the measured slope data. Conventional approaches use ordinary least squares to solve the linear equations. However, the equations are overdetermined, and the random outliers will decrease the reconstruction accuracy. The Chebyshev polynomials are suitable for discrete slope data and can be utilized to reconstruct the surface shape in deflectometry. Hence, this paper uses 2D Chebyshev polynomials as the gradient polynomial basis set. An iteratively reweighted least squares algorithm, which iteratively calculates an additional scale factor for each data point, is applied to accomplish robust linear regression. The experiments with both synthetic and measured data prove that the proposed method is robust against noise and has higher reconstruction accuracy for shape reconstruction.
Tectonostratigraphic Evolution of the Western Desert of Iraq

Abstract The study area is in the northern part of the Western Desert of Iraq. From the conclusion of Paleozoic and Mesozoic basins and their distribution according to the available geological (outcrops and boreholes) and geophysical data (seismic, gravity and magnetic interpretations controlled by geological data), it is found that, Triassic- Jurassic (and sometimes Cretaceous) basins are found closely related to the basic or ultrabasic basement (for example Anah, Tilul, and Ras Al-Fahama basins). These basins were formed by isostatic adjustment during the extension phase that took place in the Early Mesozoic (may be Triassic or Jurassic) which caused subsidence of heavy parts (basic or ultrabasic) of the basement. From the style and thickness of sediments it is clear that the northern and eastern parts of the area exhibit evidence of a tendency for uplift during Paleozoic times (forming ridges or horsts) and down-warping during the Mesozoic times (forming basins or grabens). While in contrast the western part of the area exhibits evidence or tendency for down-warping during Paleozoic forming basin or graben (as in Gaara basin) and uplift during the Mesozoic times.
Active Learning for Ordinal Classification Based on Adaptive Diversity-Based Uncertainty Sampling

Although a plethora of research has been conducted on active learning, little research attention has been focused on active learning for ordinal classification. Traditional multi-class active learning methods are typically designed for nominal multi-class classification. Therefore, they usually perform unsatisfactorily in ordinal classification settings. In ordinal classification, the cost of misclassifying an instance into an adjacent class is naturally lower than that of misclassifying it into a more disparate class. This principle is called ordering information. However, traditional active learning methods typically do not consider this ordering information during query selection. This paper proposes a novel adaptive hybrid active learning method for ordinal classification by considering the ordering information. In the proposed method, an uncertainty measure is introduced to select the hard-to-predict instances distributed between adjacent classes. In addition, a diversity measure is incorporated with the uncertainty measure to alleviate the potential sampling redundancy. Finally, an expected cost minimization measure with ordering information is designed. This measure balances the contributions of the uncertainty and diversity measures and prompts the algorithm to select the instances most likely to decrease the misclassification cost of the model. Extensive experiments on eleven public ordinal datasets demonstrate the superiority of the proposed method over several state-of-the-art methods.
Virtual Experience-Based Mobile Device Selection Algorithm for Federated Learning

In federated learning (FL), to minimize the convergence time while reaching a desired accuracy, it is important to select appropriate mobile devices (MDs) as participants with the considerations of their characteristics (e.g., data distribution, channel condition, and computing power). In this article, we first formulate a Markov decision process-based MD selection problem in which the increased accuracy per unit-time is maximized. To solve the formulated problem without any prior knowledge of the environment, a deep Q-network (DQN) algorithm can be exploited. However, storing previous experiences into the replay memory for DQN consumes an increased time since the FL server needs to conduct a number of actual FL procedures and observe the resulted rewards. To address this problem, we proposed a virtual experience-based MD selection algorithm (VE-MSA). In VE-MSA, the FL server generates virtual experiences (especially reward) without any actual FL procedures by using two neural networks approximating the round time and the increased accuracy in a round according to the selected MDs, respectively. Evaluation results demonstrate that the derived optimal policy can achieve a target accuracy within the shortest time among comparison schemes.
Human-in-the-Loop Rule Discovery for Micropost Event Detection

Platforms such as Twitter are increasingly being used for real-world event detection. Recent work often leverages event-related keywords for training machine learning based event detection models. These approaches make strong assumptions on the distribution of the relevant microposts containing the keyword – referred to as the expectation – and use it as a posterior regularization parameter during model training. Such approaches are, however, limited by the informativeness of the keywords and by the accuracy of the expectation estimation for keywords. In this work, we introduce a human-in-the-loop approach to jointly discover informative rules for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both rule-specific expectation and the disagreement between the crowd and the model in order to discover new rules that are most beneficial for model training. To identify such rules, we introduce a hybrid human-machine workflow that engages human workers in rule discovery through an interactive hypothesis creation and testing interface and leverages automatic methods for suggesting useful rules for human verification. We empirically demonstrate the merits of our approach, on multiple real-world datasets and show that our approach improves the state of the art by a margin of 25.63% in terms of AUC.
Quick Dissimilarity Detection for Center-Based Binary Images Via Smart Mapping

In this paper, we present three different smart mapping schemes that improve on the quickness of dissimilarity detection between images. We call the mapping schemes smart because the mapping order is setup intelligently to detect dissimilarity quickly by concentrating its search near the center of the images, which is usually the region of interest in a given scene. Thus, smart mapping is well suited for images when the differences between them are expected to be concentrated near the center of the image. We construct a mapping vector (MV) that contains an ordered list of point mappings which is employed to map points between images in an efficient manner. The focus in this paper is on applying the three different smart mapping schemes to binary images. Furthermore, we test three different mapping densities with each smart mapping scheme and analyze the results. Tests are conducted on two image sets and dissimilarity detection results are compared to results obtained via random mapping, which had been shown to be extremely fast, as predicted by the probabilistic matching model for binary images (PMMBI). We show that by employing smart mapping a great improvement in dissimilarity detection quickness is possible when dissimilarity between images is concentrated near the center of the scene.
An unsupervised policy relevance scoring method: Taking Chinese social security policies as the application case

Abstract Organizing and managing policy documents (PDs) issued to the public in a good way can improve the efficiency of government employees and make it easier for the public to find the needed policy information. However, existing PDs are organized only by dates and manually defined categories; besides, PDs issued by different government branches are isolated from each other. These problems make it challenging and time‐consuming for the public to find the needed policy information. We argue that implicit links should be established between PDs based on their relevance, thus helping the public find the needed policy information efficiently. To this end, we propose an unsupervised relevance scoring method for PDs consist six modules, taking Chinese social security policies as the application case. The method combines the TextRank algorithm, TF‐IDF representation, mutual information and left–right information entropy algorithm, and BERT. The method can decrease the interference of noisy words in PDs to relevance scoring. In addition, the method can consider multiple features of PDs simultaneously so that the measure of relevance can be more comprehensive. The method is not driven by domain‐specific labelled data, hence can be easily generalized to PDs in various domains. We construct a dataset containing 5000 Chinese social security policies and then conduct experiments on it to evaluate our method. Experimental results show that our method is feasible and can bring convenience to government agencies and the public to a certain extent. Furthermore, our method achieves more than a 3% improvement in evaluation results on test tasks than the methods with a similar purpose in the legal AI community.
Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter

Acquisition of images from electronic devices or Transmission of the image through any medium will cause an additional commotion. This study aims to investigate a framework for eliminating impulse noise from grayscale and medical images by utilizing linear regression and a mean filter. Linear regression is a supervised machine learning algorithm that computes the value of a dependent variable based on an independent variable. The value of the recuperating pixel is measured using a curve-fitting, direction-based linear regression approach or applying a mean filter to the noise-free pixels. The efficiency of the proposed technique experiments with benchmark test images and the images of the USC-SIPI and TESTIMAGES data sets. Peak signal-to-noise ratio and structural similarity index metrics are determined to prove the performance of the proposed method. The evaluated results, when compared with the seven recent state-of-the-art techniques, show the superiority of the proposed method in terms of visual quality and accuracy. The proposed model achieves an average PSNR value of 65.21dB and an SSIM value of 0.999 for the reconstruction of medical images, proving its accuracy and efficiency. The impulse noise restoration process helps the radiologist get a clear visual clarity of the DICOM image for diagnosis purposes.
Two Parametricities Versus Three Universal Types

The formal calculus System F models the essence of polymorphism and abstract data types, features that exist in many programming languages. The calculus’ core property is parametricity: a theorem expressing the language’s abstractions and validating important principles like information hiding and modularity. When System F is combined with features like recursive types, mutable state, continuations or exceptions, the formulation of parametricity needs to be adapted to follow suit, for example using techniques like step-indexing, Kripke world-indexing or biorthogonality. However, it is less clear how this formulation should change when System F is combined with untyped languages, gradual types, dynamic sealing and runtime type analysis (typecase) alongside type generation. Extensions of System F with these features have been proven to satisfy forms of parametricity (with Kripke worlds carrying semantic interpretations of types). However, the relative power of the modified formulations of parametricity with respect to others and the relative expressiveness of System F with and without these extensions are unknown. In this paper, we explain that the aforementioned different settings have a common characteristic: they do not enforce or preserve the lexical scope of System F’s type variables. Formally, this results in the existence of a universal type (note: this is not the same as a universally-quantified type). We explain why standard parametricity is incompatible with such a type and how type worlds resolve this. Building on these insights, we answer two open conjectures from the literature, negatively, and we point out a deficiency in current proposals for combining System F with gradual types.
Construct maximally seven-qubit entangled state via Brown state

It is worth studying recurrence relation of multi-qubit states due to its benefits to the criterion and measurement of quantum entanglement. Recently, Huang et al. presented an expand recurrence relation of multi-qubit state. Using this expand recurrence relation and Brown state, we construct a new seven-qubit maximally entangled states of 32 and 3 balanced purities equal to 1/8 and 1/4, whose averaged subsystem purity is equal to 19/140. We found that the marginal density matrices for subsystems of 1 and 2 qubits all completely mixed in these states.
Study on an architecture of ontology-based task modeling and deduction for automotive troubleshooting service

The rapid development of today’s smart automobile with increasing functionality and complexity, which results in rising requirement and cost for its troubleshooting service in the after-sales stage. Hence, to keep the firm’s competitive strength, effective design knowledge utilization of automotive created from the task model can promote the feasibility making of a troubleshooting procedure by offering available relationships and semantic scheme of task. The proposed architecture primarily consists of base, field and application three tiers. A formalized representation of ontology, OWL, is used to organize the base filed. The filed tier includes extensional notions and relations for integration of troubleshooting and a criterion repository for verifying solution feasibility, which is depicted in SWRL. In the application tier, a deducing module is generated on the basis of ontology and criterion deduction. To enhance this semantics, in this research, a task modeling and deduction mechanism with feature-intensive ontology are proposed to clearly represent correlative notions for automotive troubleshooting planning (ATP). A criterion-based deducing module on the basis of OWL-DL and SWRL is also applied to specify implied relations through merging deducing modules (DMs) to deal with complex feature data. Eventually, this proposed architecture is examined and verified with an instance relevant to automotive troubleshooting procedure.
Saturation attack on discretely modulated continuous-variable quantum key distribution over an atmospheric turbulence channel

Continuous-variable quantum key distribution (CVQKD) over an atmosphere channel provides the possibility of global coverage, which is threatened by the imperfection of actual devices. Exploiting imperfections of practical devices, an eavesdropper steals the information between legal participants by performing the potential saturation-induced attacks on the homodyne detector. In this paper, we demonstrate the feasibility of the potential saturation attack in discretely modulated (DM) CVQKD over atmospheric turbulence. Subsequently, a countermeasure is suggested by using an adjustable optical filter (AOF) to attenuate the estimated parameter with the measured value in the finite linearity domain, where the attenuated data can be compensated. Numerical simulations illustrate the effects of the saturation attack on the performance of the AOF-based CVQKD system. We find that legal participants evaluate the information that has been eavesdropped because the secret key rate is made negative.
Optical design of a solar simulator for meteorological fields to improve irradiance uniformity and expand the range of irradiation regulation

Aiming at the problems of low irradiation uniformity and a narrow irradiance regulation range in the existing solar simulators, an optical design method for meteorological solar simulators with high irradiation uniformity and wide-range irradiance is proposed. Using a xenon lamp and a variable coefficient non-coaxial ellipsoid reflector as the concentrator system, we analyze the causes of stray light in the optical integrator. The optimal design method of the integrator based on the anti-crosstalk diaphragm and the light propagation matrix model, which effectively suppress the stray light, is proposed. The irradiance regulation system is designed to continuously regulate the irradiance in a wide range. The optimal design method of the collimated system is given. The rationality of the system design is verified by the simulation of LightTools software. The results show that within the effective irradiation surface of 100mm×100mm, the irradiance is continuously adjustable in the range of 100-1400W/m2, and the irradiation uniformity is better than 99.10% under different irradiances. This research breaks through the limitations of low irradiation uniformity and a narrow irradiance adjustment range of traditional meteorological solar simulators and can provide accurate and reliable solar irradiance for the verification and calibration of pyranometers.
Ethereum Ponzi Scheme Detection Based on PD-SECR

Ethereum, a typical application of blockchain technology, has attracted extensive attention from all walks of life since its release. Owing to imperfections in existing supervision technology, illegal and criminal activities on blockchain platforms are becoming increasingly frequent. The most typical Ethereum fraud is the Ponzi scheme, which causes blockchain investors to lose millions of assets and severely impacts social development. Currently, Ponzi scheme detection primarily focuses on machine learning and data mining. However, existing detection methods still have two problems in data imbalance processing and feature extraction: (1) data enhancement using an oversampling algorithm produces noise and (2) feature redundancy existing in extracted feature data. The SMOTEENN algorithm is introduced to solve data imbalance. The PD-SECR method, the Convolutional Neural Network (CNN) feature extraction, and random forest (RF) classification models are used for detection, but the two models are independently trained. The results show that the detection method proposed in this study is more suitable for the Ethereum Ponzi scheme.
ENHANCEMENT OF SECURED DATA TRANSMISSION USING n*3 INCOMPREHENSIBLE STEGO FILES BASED ON LS, SL, AND LC ALGORITHMS

In this paper, security of covert communication is increased by using &lt;i&gt;n&lt;/i&gt;&lt;sup&gt;*&lt;/sup&gt;3 incomprehensible stego files based on the novel algorithms, which are logical and statistical (LS), split logical (SL), and logical concealing (LC). Generally, cryptography and steganography techniques are used to achieve a secured data transmission. Cryptography is used to generate the "ciphertext" and steganography is used to produce the understandable stego image. The results of these two techniques alert the intruder to the fact that secret information is being exchanged. To overcome this drawback, this paper generates n*3 incomprehensible stego files with four levels of security using covert data and a container (cover) image. The main novelty of the proposed framework is that it provides multilevel security for data transmission by converting understandable stego images into non-understandable (incomprehensible) stego files. The LS encryption algorithm generates the lesser length of ciphertext with the input of covert data. The steganography generates a good visual quality of an understandable stego image by using discrete framelet transform (DFT) and random pixel embedding (RPE) techniques. Finally, the SL and LC algorithms produce &lt;i&gt;n&lt;/i&gt;&lt;sup&gt;*&lt;/sup&gt;3 incomprehensible stego files using the understandable stego image. These stego files are incomprehensible, meaningless, and invisible in nature. The experimental results have shown that the incomprehensible stego files improve the payload capacity, quality metrics (peak signal-to-noise ratio, correlation, and number of pixels change in rate), and also four levels of security to the secret data. It is also able to face the various steganalysis attacks, such as files deletion, data deletion, and data modification.
Utilisation of Electronic Resources by Undergraduate Students at the Namibia University of Science and Technology (NUST)

The study that directed this article examined the use of electronic resources by undergraduate students at the Namibia University of Science and Technology (NUST). A survey-based research design was employed to collect quantitative and qualitative data—the quantitative paradigm being dominant. The population for this study consisted of undergraduate students at the NUST main campus (10 248) registered for the 2016 academic year. A stratified random sampling technique was used to select students from each of the four undergraduate levels of study. A sample size of 385 students was used. A self-developed questionnaire and a structured interview guide were used as data collection instruments. Responses from 274 questionnaires and 12 face-to-face interviews provided the raw data. The study showed that subscribed electronic resources were underutilised due to a lack of awareness. Students had positive attitudes towards electronic resources for academic purposes, research projects and keeping up-to-date. While the vast majority of students were confident in using computers, they lacked the search skills necessary to make full use of electronic resources. Barriers encountered included poor ICT infrastructure and internet connectivity, as well as slow, old, and inadequate computers. The recommendations included the need for improved ICT facilities and internet connectivity, regular information needs-assessment, end-user training, and the vigorous promotion of e-resources.
A survey on deep learning techniques in real-time applications

In recent years, machine learning and Deep Learning have increased and gathered epic success in traditional application domains and new areas of Artificial Intelligence. The performance using Deep Learning has dominated experimental results compared to conventional machine learning algorithms. This paper presents an overview of the progress that has occurred in Deep Learning (DL) concerning some application domains like Autonomous Driving, Healthcare, Voice Recognition, Image Recognition, Advertising, Predicting Natural Calamities, National Stock Exchange and many more. Additionally, deeper insights into several Deep Learning techniques, their working principles, and experimental results are scrutinized. The survey covers Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL).