Reviewerly/exHarmony huggingface dataset:
A Feature-Based Method for Detecting Design Patterns in Source Code

Design patterns are common solutions to existing issues in software engineering. In recent decades, design patterns have been researched intensively because they increase the quality factors of software systems such as flexibility, maintainability, and reusability. Design pattern detection refers to the determination of the symmetry between a code fragment and the definition of a design pattern. One of the major challenges in design pattern detection is how to obtain accurate information about the design patterns used in the software system due to the existence of different design pattern variants. Increasing the number of design pattern variants covered by a detection method is one of the main factors that increase its accuracy. In this paper, a step toward solving this challenge was taken by proposing a new feature-based method that builds on concrete definitions of existing design pattern variants and supports the definition and detection of new variants. In this proposed method, the needed features are extracted from the signatures of the design patterns. This method was applied to the 23 Gang of Four (GoF) design patterns and evaluated using four open-source Java projects. Afterward, it was compared with some previous methods using automatically generated testbeds. The experimental results demonstrated that the proposed method has better performance in terms of precision and recall compared to the other methods.
Student Performance Prediction in Sebelas Maret University Based on the Random Forest Algorithm

Students who have low levels of academic performance may result in such students having drop out. Various factors influence the level of academic performance of such students. Preventive action would be better to cope with the drop out. This study aims to conduct prediction of students' academic performance at Sebelas Maret University based on three categories of factors namely social, economic, and academic factors. Methods used include, data acquisition stages, data preprocessing, feature selection, classification, and analysis of results. Feature selection uses information gain (IG) and Random Forest (RF) classification algorithms, with 10-fold cross validation. The test results showed an accuracy of 90.7%, such performance outperforms support vector machine (SVM) and Decision Tree (DT) algorithms.
Arabic Sentiment Analysis of Eateries’ Reviews Using Deep Learning

After air and water, food is the third most essential thing for humans to provide energy and development. More and more customers of eateries, including restaurants and cafes, express their opinion or sentiment about quality, ambiance, and facilities. This research performs a sentiment analysis of the eateries’ reviews obtained in Qassim, Saudi Arabia. The reviews are obtained in Arabic, the local language of the region. We apply various models of Long Short-Term Memory, a deep learning technique. The best approach achieved 83% accuracy. Furthermore, we also compared the proposed methods with state-of-the-art machine learning ones, such as support vector machines, nearest neighbor, Naïve Bayes, random forest, and logistic regression. The achieved results are promising.
A New Supervised Term Weight Measure Based Approach for Text Classification

The textual information is abundantly increasing in the internet through different types of social media platforms. Knowing the type of information is one challenging task to different information retrieval systems and researchers. Text classification is one research domain used to categorize the textual information into different classes. Most of the researchers proposed approaches based on the content used in the textual documents. Identification of appropriate terms for differentiating the text is one important task in text classification. After identification of terms for experiment, next very important task is determining the importance of a term in document representation. The term weight measures are used for finding the importance of a term in a document. In this work, a new supervised term weight measure named as TF-NRF-IPNDF-PNDDF is proposed. The performance of proposed term weight measure is compared with eight popular term weight measures such as TFIDF, TFIEF, TFRF, TF-IDF-ICSDF, TF-PROB, TF-IGM, CDallc and CDc. The experiment conducted on six standard classification datasets such as IMDB, HSS, FN, 20NG, AGN and CBN. Six different classification algorithms such as KNN, NB, LR, SVM, DT and RF are used for evaluating the performance of the proposed term weight measure. The proposed term weight measure attained best accuracies for different standard datasets compared with other term weight measures.
A Study of the Optical Bandgap Energy and Urbach Energy Tail of Two White A4 Copy Paper Samples

Annals of Chemical Science Research A Study of the Optical Bandgap Energy and Urbach Energy Tail of Two White A4 Copy Paper Samples Katerina Chryssou*, Maria Stassinopoulou and Eugenia Lampi General Chemical State Laboratory, B’ Chemical Division of Athens, Greece *Corresponding author: Katerina Chryssou, General Chemical State Laboratory, B’ Chemical Division of Athens, Department A’ Tsocha 16, Greece Submission: November 13, 2021;Published: December 02, 2021 DOI: 10.31031/ACSR.2021.03.000551 Volume2 Issue5November, 2021
Image Encryption Algorithm Using 2-Order Bit Compass Coding and Chaotic Mapping

This paper proposes a novel image encryption algorithm based on an integer form of chaotic mapping and 2-order bit compass diffusion technique. Chaotic mapping has been widely used in image encryption. If the floating-point number generated by chaotic mapping is applied to image encryption algorithm, it will slow encryption and increase the difficulty of hardware implementation. An innovative pseudo-random integer sequence generator is proposed. In chaotic system, the result of one-iteration is used as the shift value of two binary sequences, the original symmetry relationship is changed, and then XOR operation is performed to generate a new binary sequence. Multiple iterations can generate pseudo-random integer sequences. Here integer sequences have been used in scrambling of pixel positions. Meanwhile, this paper demonstrates that there is an inverse operation in the XOR operation of two binary sequences. A new pixel diffusion technique based on bit compass coding is proposed. The key vector of the algorithm comes from the original image and is hidden by image encryption. The efficiency of our proposed method in encrypting a large number of images is evaluated using security analysis and time complexity. The performance evaluation of algorithm includes key space, histogram differential attacks, gray value distribution(GDV),correlation coefficient, PSNR, entropy, and sensitivity. The comparison between the results of coefficient, entropy, PSNR, GDV, and time complexity further proves the effectiveness of the algorithm.
Vehicle Detection from Unmanned Aerial Images with Deep Mask R-CNN

In this paper, a classification approach which is applied to Mask Region-based Convolutional Neural Network as deeper is proposed for vehicle detection on the images from UAV instead of the familiar methods. The different types of unmanned aerial vehicles are widely used for a lot of areas such as agricultural spraying, advertisement shooting, fire extinguishing, transportation and surveillance, exploration, destruction for the military. In recent years, deep learning techniques are progressively developed for object detection. Segmentation algorithms based on CNN architecture are especially widely used for extracting meaningful parts of an image. Additionally, Mask R-CNN based on CNN architecture rapidly detects the object with high-accuracy on an image. This study shows that the high-accuracy results are obtained when the Mask R-CNN is applied as deeper in vehicle detection on the images taken by UAV.
Blockchain Potentials to Enhance Identified Data Usability Challenges Generated by Wearables

Blockchain, the underlying technology behind cryptocurrency, has found use in many industries, such as healthcare, where Internet-of-Things (IoT)-enabled wearables and home use medical devices allow patient data to be collected by the patient themselves, which causes concerns related to data generation, transmission, and storage. The purpose of this quantitative study is to increase the understanding of what concerns medical professionals have about data usability generated by these devices, and if blockchain can eliminate these challenges. An online survey was distributed to 29 medical professionals. The findings that surveyed participants showed that significant concerns that the patients' IT skills, device malfunction, data privacy, the patient's exam skills and environment during the exam are factors that may affect the patient health data. Upon analyzing blockchain capabilities, it was discovered that it can be used as a solution to some of the identified hurdles and help to comply to the health data priorities in healthcare ethics.
Deep Transfer Learning Method Based on Automatic Domain Alignment and Moment Matching

Domain discrepancy is a key research problem in the field of deep domain adaptation. Two main strategies are used to reduce the discrepancy: the parametric method and the nonparametric method. Both methods have achieved good results in practical applications. However, research on whether the combination of the two can further reduce domain discrepancy has not been conducted. Therefore, in this paper, a deep transfer learning method based on automatic domain alignment and moment matching (DA-MM) is proposed. First, an automatic domain alignment layer is embedded in the front of each domain-specific layer of a neural network structure to preliminarily align the source and target domains. Then, a moment matching measure (such as MMD distance) is added between every domain-specific layer to map the source and target domain features output by the alignment layer to a common reproduced Hilbert space. The results of an extensive experimental analysis over several public benchmarks show that DA-MM can reduce the distribution discrepancy between the two domains and improve the domain adaptation performance.
Estimating Quantum Mutual Information of Continuous-Variable Quantum States by Measuring Purity and Covariance Matrix

We derive accessible upper and lower bounds for continuous-variable (CV) quantum states on quantum mutual information. The derivations are based on the observation that some functions of purities bound the difference between quantum mutual information of a quantum state and its Gaussian reference. The bounds are efficiently obtainable by measuring purities and the covariance matrix without multimode quantum state reconstruction. We extend our approach to the upper and lower bounds for the quantum total correlation of CV multimode quantum states. Furthermore, we investigate the relations of the bounds for the quantum mutual information with the bounds for the quantum conditional entropy.
A Fast and Close-to-Optimal Receding Horizon Control for Trajectory Generation in Dynamic Environments

This paper presents a new approach for the optimal trajectory planning of nonlinear systems in a dynamic environment. Given the start and end goals with an objective function, the problem is to find an optimal trajectory from start to end that minimizes the objective while taking into account the changes in the environment. One of the main challenges here is that the optimal control sequence needs to be computed in a limited amount of time and needs to be adapted on-the-fly. The control method presented in this work has two stages: the first-order gradient algorithm is used at the beginning to compute an initial guess of the control sequence that satisfies the constraints but is not yet optimal; then, sequential action control is used to optimize only the portion of the control sequence that will be applied on the system in the next iteration. This helps to reduce the computational effort while still being optimal with regard to the objective; thus, the proposed approach is more applicable for online computation as well as dealing with dynamic environments. We also show that under mild conditions, the proposed controller is asymptotically stable. Different simulated results demonstrate the capability of the controller in terms of solving various tracking problems for different systems under the existence of dynamic obstacles. The proposed method is also compared to the related indirect optimal control approach and sequential action control in terms of cost and computation time to evaluate the improvement of the proposed method.
Transitional dynamics and the evolution of information transparency: a global analysis

The last quarter of the 20th century was a period of sustained economic growth across many countries. Countriesâ institutional arrangements have been commonly employed as factors in the convergence studies of economic growth and income levels. However, the issue of whether institutions themselves converge has been under-researched. Using the nonparametric distribution dynamics approach and a sample of 194 countries during the 1980-2010 period, we examine a tendency for countriesâ informational transparency (IT) to converge over time. We find that whilst there is some evidence of unconditional convergence across countries, there is stronger evidence for convergence clubs to emerge, at both regional and income levels. Notably, the level of IT of the low- and lower-middle-income countries and those situated in Africa, and Middle East regions tend to converge towards a level significantly below the global average. We also find a strong relationship between income and IT.
Analysis of Thickness Distributions Calculated from Surface Strains Obtained through Digital Image Correlation for Incremental Sheet Forming

This paper discusses the thickness distributions calculated from surface strain measurements using stereo Digital Image Correlations (DIC) for parts produced with Single Point Incremental Forming (SPIF). The research is carried out on six benchmark cones and pyramids with each convex, straight and concave walls. The accuracy of the thickness calculations, under the assumption of material incompressibility and using the formula for the Green-Lagrange strains, is compared to the thickness distributions measured with a fringe projection scanner. The thickness estimations based on the measured strains proved to be representative for the measured thickness distributions with a mean error of 0.0182 mm, which corresponds to a relative error of 1.47 % of the mean measured thickness. However, errors of up to 0.1688 mm were found in areas of high wall angles and curvatures, corresponding to a relative maximal error of 13.69 % of the mean measured thickness. Hence, the DIC measurements are well suited for characterizing the thickness. Using the thicknesses calculated from the DIC measurements to find the minimal thickness as an indicator of part failure, is possible with relative errors that have an average overestimation of 2.87% of the minimal measured thickness.
Willingness to be the recipient during the dictator game

Researchers have investigated human altruism toward strangers for decades, using economic games such as the dictator game (DG) in their experiments. However, factors that cause the allocating behavior exhibited by those participants willing to be recipients in the DG have not been identified and the psychological mechanism of avoiding decision-making in economic games has not been widely addressed in previous studies. This study aimed to replicate previous findings regarding the number of people who are willing to be assigned the role of recipient and their allocation behavior and to explore why they share more than people who are willing to be dictators.We demonstrate that there are people willing to be assigned the role of the recipient, rather than the role of the dictator during the dictator game. In addition, we find evidence indicating that people who are willing to be recipients behave more altruistically in the dictator game than those who prefer to be dictators. Based on our results, we argue that willingness to be a recipient, in relation to the psychological unwillingness to assume responsibility and reputational concerns, is a strategic consideration.
3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion

Abstract Background Cervical cancer cell detection is an essential means of cervical cancer screening. However, for thin-prep cytology test (TCT)-based images, the detection accuracies of traditional computer-aided detection algorithms are typically low due to the overlapping of cells with blurred cytoplasmic boundaries. Some typical deep learning-based detection methods, e.g., ResNets and Inception-V3, are not always efficient for cervical images due to the differences between cervical cancer cell images and natural images. As a result, these traditional networks are difficult to directly apply to the clinical practice of cervical cancer screening. Method We propose a cervical cancer cell detection network (3cDe-Net) based on an improved backbone network and multiscale feature fusion; the proposed network consists of the backbone network and a detection head. In the backbone network, a dilated convolution and a group convolution are introduced to improve the resolution and expression ability of the model. In the detection head, multiscale features are obtained based on a feature pyramid fusion network to ensure the accurate capture of small cells; then, based on the Faster region-based convolutional neural network (R-CNN), adaptive cervical cancer cell anchors are generated via unsupervised clustering. Furthermore, a new balanced L1-based loss function is defined, which reduces the unbalanced sample contribution loss. Result Baselines including ResNet-50, ResNet-101, Inception-v3, ResNet-152 and the feature concatenation network are used on two different datasets (the Data-T and Herlev datasets), and the final quantitative results show the effectiveness of the proposed dilated convolution ResNet (DC-ResNet) backbone network. Furthermore, experiments conducted on both datasets show that the proposed 3cDe-Net, based on the optimal anchors, the defined new loss function, and DC-ResNet, outperforms existing methods and achieves a mean average precision (mAP) of 50.4%. By performing a horizontal comparison of the cells on an image, the category and location information of cancer cells can be obtained concurrently. Conclusion The proposed 3cDe-Net can detect cancer cells and their locations on multicell pictures. The model directly processes and analyses samples at the picture level rather than at the cellular level, which is more efficient. In clinical settings, the mechanical workloads of doctors can be reduced, and their focus can be placed on higher-level review work.
Modelling and Mapping of Soil Organic Matter in Doukkala Plain, Moroccan Semi-Arid Region

The Doukkala plains one of the largest irrigated areas in Morocco with a very important agricultural potential.With the integration of new technologies in agriculture, the plain has been subjected to intensive agriculture which has negative impacts on soil quality especially the soil organic matter loss.Therefore, the objective of this study is to combine remote sensing and modelling for monitoring of organic matter content.The obtained results showed that all the examined models showed satisfactory results in the prediction of organic matter with a coefficient of determination R 2 ranging from 0.58 to 0.71 and the Root Mean Square Error (RMSE) varied 0.25 and 0.26%.Based on the findings, we can infer that this approach is both efficient and valid for modelling and mapping soil organic matter and may moreover be applied for other areas with same characteristics.
Translation Errors in Studentsâ€™ Indonesian-English Translation Practice

This article discusses the translation errors in Indonesian-English translation practice that were made by the students. It is based on the research in English Education Undergraduate Program, UNNES. Descriptive qualitative research method, specifically qualitative data analysis was used in this research. The subjects of the study were the students in Indonesian-English Translation Course. The object of the research was the Indonesian-English translation product, produced by the students. The object of the study was translation products which were produced by the students. The data were collected by using a translation test, questionnaires, and interviews. Moreover, the data were analyzed using qualitative data analysis. The result of the analysis showed that there were 21 translation errors of 26 error categories according to ATAâ€™s Framework for Standardized Error Making. In conclusion, the three most prominent error categories made by the students were grammar, syntax, and faithfulness error. The errors were mostly influenced by extratextual factors such as sender, intention, recipient, medium, time, motive, and text function. In addition, lexis was also an influencing factor in making errors.
Two-step adversarial debiasing with partial learning -- medical image case-studies

The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years. While significant progress has been made in image classification tasks, only a few AI methods are actually being deployed in hospitals. A major hurdle in actively using clinical AI models currently is the trustworthiness of these models. More often than not, these complex models are black boxes in which promising results are generated. However, when scrutinized, these models begin to reveal implicit biases during the decision making, such as detecting race and having bias towards ethnic groups and subpopulations. In our ongoing study, we develop a two-step adversarial debiasing approach with partial learning that can reduce the racial disparity while preserving the performance of the targeted task. The methodology has been evaluated on two independent medical image case-studies - chest X-ray and mammograms, and showed promises in bias reduction while preserving the targeted performance.
Fragmented imaginary-time evolution for early-stage quantum signal processors

Simulating quantum imaginary-time evolution (QITE) is a major promise of quantum computation. However, the known algorithms are either probabilistic (repeat until success) with impractically small success probabilities or coherent (quantum amplitude amplification) but with circuit depths and ancillary-qubit numbers unrealistically large in the mid term. Our main contribution is a new generation of deterministic, high-precision QITE algorithms significantly more amenable experimentally. These are based on a surprisingly simple idea: partitioning the evolution into several fragments that are sequentially run probabilistically. This causes a huge reduction in wasted circuit depth every time a run fails. Indeed, the resulting overall runtime is asymptotically better than in coherent approaches and the hardware requirements even milder than in probabilistic ones, remarkably. More technically, we present two QITE-circuit sub-routines with excellent complexity scalings. One of them is optimal in ancillary-qubit overhead (one single ancillary qubit throughout) whereas the other one is optimal in runtime for small inverse temperature or high precision. The latter is shown by noting that the runtime saturates a cooling-speed limit that is the imaginary-time counterpart of the no fast-forwarding theorem of real-time simulations, which we prove. Moreover, we also make two technical contributions to the quantum signal processing formalism for operator-function synthesis (on which our sub-routines are based) that are useful beyond QITE. Our findings are specially relevant for the early fault-tolerance stages of quantum hardware.