Reviewerly/exHarmony huggingface dataset:
Hierarchical Iris Image Super Resolution based on Wavelet Transform

Iris images under the surveillance scenario are often low-quality, which makes the iris recognition challenging. Recently, deep learning-based methods are adopted to enhance the quality of iris images and achieve remarkable performance. However, these methods ignore the characteristics of the iris texture, which is important for iris recognition. In order to restore richer texture details, we propose a super-resolution network based on Wavelet with Transformer and Residual Attention Network (WTRAN). Specifically, we treat the low-resolution images as the low-frequency wavelet coefficients after wavelet decomposition and predict the corresponding high-frequency wavelet coefficients sequence. In order to extract detailed local features, we adopt both channel and spatial attention, and propose a Residual Dense Attention Block (RDAB). Furthermore, we propose a Convolutional Transformer Attention Module (CTAM) to integrate transformer and CNN to extract both the global topology and local texture details. In addition to constraining the quality of image generation, effective identity preserving constraints are also used to ensure the consistency of the super-resolution images in the high-level semantic space. Extensive experiments show that the proposed method has achieved competitive iris image super resolution results compared with the most advanced super-resolution method.
YOLO-oil: A Real-time Transformer Fault Detector toward Small Dataset

In electrical substations, fault detection of the transformer widely relies on human eye, which is low efficiency and costly. With under-oil robot and deep learning algorithms, the fault detection will be done without draining the transformer oil. As we know, deep learning methods for computer vision have achieved incredible results on some tasks such as object detection. However, such success greatly relies on the huge dataset, which is extremely high-cost and unavailable in some industry application. Deep learning algorithm, such as YOLO series, often fails on small dataset, and the test accuracy decreases significantly due to the neural network overfitting on the small dataset. In this paper, the YOLO-oil network for transformer fault detector based on YOLOv5 is proposed to mitigate the overfitting problem on small dataset: First, we shrink the network depth and get a light weight backbone. Second, we improved the network architecture by decoupling the detect head network. Since no open dataset exists for transformer fault detection before, the author creates a brand-new training dataset and a test dataset. Experimental results on the test set show that our algorithm achieves surprising results for the transformer fault detection task and surpasses YOLOv5, which is a great help to industry application.
An Improved Dark Channel Prior Defogging Algorithm Based on Transmissivity Image Segmentation

In view of the dark channel prior algorithm in dealing with the haze image color distortion in the sky region, atmospheric light value error extraction and scene edge halo effect, a dark channel prior defogging removal method based on transmittance image is proposed in this paper. The input haze image is converted into transmittance image. with guided filtering, the improved MSR algorithm can be used to segment the image into sky region and non-sky region. Minimum filtering and sky transmittance estimation are performed for sky region and non-sky region respectively. The two parts of images obtained by processing are combined, and the transmission is refined by fast guided filtering, and the haze image is defogging removed by combining the atmospheric light value extracted from the sky region to obtain a clear restored image. The experimental results show that the improved minimum filtering algorithm and transmittance estimation method can effectively remove the halo effect at the edge of the depth of field and the color distortion in the sky area, so that the restored image retains more details and has a clearer and natural vision. Compared with the traditional dark channel prior algorithm, the information entropy of the proposed algorithm increases by 12.1% on average, PNSR increases by 6.024% on average, SSIM increases by 15.8%, and MSE decreases by 4.7% on average.
PENGARUH DISIPLIN BELAJAR TERHADAP PRESTASI BELAJAR PESERTA DIDIK DI MTS MA’ARIF NU 04 DESA KLADI KECAMATAN CERMEE BONDOWOSO

This research was conducted with the aim of improving students' learning discipline. Learning is an important process for changing human behavior and includes everything that is thought and done. To measure whether someone has learned, it is necessary to compare the behavior before and after experiencing learning. Discipline is someone who learns to voluntarily follow the leader. Parents and teachers are leaders and children are students towards a better and more useful life. Using a quantitative approach with ex post facto research involving 60 students in even semesters at MTs Ma'arif Nu 04 Kladi Village, Cermee District, Bondowoso Regency. The results of this study are divided into three, namely, (1.) There is an influence of learning discipline (X) on student learning achievement (Y) at MTs Ma'arif nu 04 Kladi Village, Cermee Subdistrict, Bondowoso Regency for the 2020/2021 academic year. From the research results obtained using the regression line analysis of variance formula yielding Ry (1,2) of 0.537 Which when consulted with the r table with N as many as 60 respondents and a significant level of 5% obtained 0.254 the calculation is intended to prove the major working hypothesis by using the test significant.
The Survey of Object-Oriented Software Programming Language from a Heterogeneous Cluster Programming Viewpoint

In this paper, the problem of programming language selection is presented from the position of large cluster with heterogeneous accelerators programming in the situations when it is need to apply object-oriented programming like in the case of heterogeneous multi-agent simulations or large data modelling using memetic algorithms. This work was inspired by experience with the Chapel language obtained during complicated conversion of hybrid evolutionary algorithm GPAes from a single node OpenMP C++ implementation onto HPC cluster with nodes equipped by both CPU and GPGPU. The paper consists of discussion of many approaches to parallel programming including not only traditional ways such as OpenMP, MPI and Cuda and their combinations, but also modern extensions of C/C++ as OpenACC, Silk and CYCL. Emerging languages as Chapel and Julia are discussed too. The work concludes with an evaluation of the real state of parallel object-oriented programming on heterogeneous node HPC clusters.
Reliability Assessment of Segments of the Digital Ecosystems

The problems that arise when setting tasks for determining the reliability of segments of digital ecosystems are considered. The features of determining the reliability of digital ecosystems during preliminary design and during test operation are indicated. Varieties of different methods are presented and characterized, which simplify the assessment of the risk of manifestation of anomalies used in the analysis of the reliability of information systems, their suitability for various procedures for determining reliability is considered. The features of the formation of the error propagation vector and the problems of functional redundancy of user tools are considered, considering their probabilistic indicators in the preliminary design and during the test operation. When constructing the error vector, the specifics of the manifestation of the residual risk of the filtering mechanisms and the formation of new vectors are determined. The formation of the vector is considered considering the specifics of the multi-level organization of digital ecosystems, differences in technologies used at different levels, including industrial networks. The specificity of a simplified model for calculating reliability is determined, which includes procedures focused on various approaches to analyzing the risk of problems in the operation of digital ecosystems. Calculation models are given that consider the failure rate of ecosystem segments, each segment or means of ensuring the reliability of the functioning of digital ecosystem components. The specifics of determining the magnitude of the risk of disconnection of each object of the ecosystem is considered. The features of the interpretation of the value of the success of the reliability assurance tools in digital ecosystems are determined.
Towards a Blockchain Solution for Customs Duty-Related Fraud

Customs duties are meant to control the flow of goods across international borders and represent a significant source of government revenue. In many parts of the world, the enforcement of customs duties is labor-intensive and fraught with inefficiencies, fraud, and corruption, leading to a substantial loss of revenue. We propose a blockchain model built on Hyperledger Fabric that can reduce some of these hindrances and pay for itself quickly through more efficient transactions and a reduction in opportunities for fraud. We illustrate how permissionless blockchain models are a better fit for international trade and show how ordered transactions in a blockchain can help reduce fraud related to avoiding duties on the import and export of goods. Last, we conduct some benchmarking on the scalability of our proposed model and examine some of the effects it may have on performance.
A Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine

The imbalanced classification is an important branch of supervised learning and plays the important roles in many application fields. Compared with the sophisticated improvements on classification algorithms, it is easier to obtain the good performance by synthesizing the minority class samples so that the classification algorithms can be trained based on the balanced data sets. In consideration of the strong representation ability of multi-layer extreme learning machine (MLELM), this paper proposes a new method to create the synthetic minority class samples based on auto-encoder ML-ELM (simplified as AE-MLELM-SynMin). Firstly, an AE-MLELM is trained to obtain the deep feature encodings of original minority class samples. Secondly, the crossover and mutation operations are preformed on the original deep feature encodings and a number of new deep feature encodings are generated. Thirdly, the synthetic minority class samples are created by transforming the new deep feature encodings with AE-MLELM. Finally, the persuasive experiments are conducted to demonstrate the effectiveness of AE-MLELM-SynMin method. The experimental results show that our method can obtain the better imbalanced classification performance than SMOTE, Borderline-SMOTE, Random-SMOTE, and SMOTE-IPF methods.
A Framework for Analyzing Context-Oriented Programming Languages

Context-aware systems keep on emerging as an intrinsic part of everyday activities. To cope with such situations, programming languages are extended to support the notion of context developing the context-oriented programming paradigm. Although context-oriented programming languages exist for over 15 years, they were tested for their suitability in developing context-aware systems only to a limited extent. In this paper, we propose a framework for analyzing context-oriented languages from that viewpoint. Using this framework, we are able to examine context definition and activation, reasoning capabilities, process aspects of how to work with the language, and other pragmatic considerations. To demonstrate the use of the framework, we apply it to analyze three context-oriented programming languages: ServalCJ, Subjective-C, and COBPjs which represent the major Context-Oriented Programming themes. We evaluate the capabilities of each language using the purposed framework. Developers of context-oriented programming languages can use the framework to improve their languages and the associated development and supporting tools. Furthermore. such analysis can support users of context-oriented programming languages in deciding the language that best suits their needs.
Comparative Analysis of Mikado Method Approach to Refactor Legacy Code to Common Approach in Software Quality Measuring

As a software engineer, facing legacy code can be challenging. Most of the time, software engineers hate legacy code. But at some point, to maintain our services, we are forced to refactor our legacy code because of many factors such as obsolete dependencies, version conflict, lousy performance, etc. Mikado Method is a way to simplify the basic needs of a software engineer to revamp complex code through a structured approach. Mikado Method has four basics: Set a Goal, Experiments, Visualizations, and Undo. These frameworks exist in Mikado Method to help software engineers to reconstruct a legacy code free of hassle. With the help of the Mikado Method, software engineers will be able to deal with legacy codes without fearing code smell. Additionally, by refactoring the Legacy Code, Software Engineers will achieve better-quality software than the old one. Thus, this paper concludes that Mikado Method will ensure engineers with a more consistent and planned refactoring of the codes compared with other common approaches with the same quality of codes or better.
A Comprehensive Survey on Multilingual Opinion Mining

In a current scenario use of multimedia, gadgets have increased the usage of social websites and the Internet. Twitter, Facebook, Instagram, Telegram, and WhatsApp are the generally used platforms in the Internet community. Sharing reviews, feedbacks, and personal experiences are the most common task on social media. Such data is available in an unorganized and immensurable manner on the Internet. Opinion Mining can be carried out on such data available on the Internet. Most of the analyzers are working on the analysis of Chinese and English language sentiments, data available on the Internet is also in different languages which needs to be analyzed. The main purpose of this paper is to discuss the different frameworks, algorithms, Opinion Mining processes, classification techniques, evaluation methods, and limitations faced by the analyzers while bringing off the sentiment analysis on different languages.
A Novel Dual-Watermarking Approach for Authentication and Tamper Recovery of Colored Images

In the world of Internet communication, one of the obligatory aspects of concern is data security. An estimated 2.5 quintillion bytes of data are generated every day. This data include images, videos, voice messages, and most importantly documents. Transfer of important and even confidential documents is increasing every day, and with this comes the problem of authenticity. While documents are being transferred, there is a high risk involved of data contained in them being tampered. This can have serious implications in the financial, health care, manufacturing, construction, retail, and other sectors. To solve this problem, we have introduced an approach to effectively watermark color document images and to detect image tampering as well as recover the tampered regions. Out new dual-watermarking approach uses 2 × 2 blocks to have minimum blurring in recovered region while using RGB as the color space of action. Our approach uses a separate file to contain the secret information that let us reach a block size as small as 2 × 2. This secret file does not contain any information related to the data of a block but the information related to recovering it. The 4 × 3 bits generated as watermark per block are randomly permuted and hence making it impossible to detect the process of watermarking on an image. Also, it is impossible to recover the image by just using the secret file or the image itself. The unique pair of secret and watermarked image is the key to the success of this algorithm. Therefore, our method can be easily and best-suitably used for high confidentiality applications.
Re-evaluation of the CNN-based state-of-the-art crowd-counting methods with enhancements

Crowd counting has a range of applications and it is an important task that can help with the accident prevention such as crowd crushes and stampedes in political protests, concerts, sports, and other social events. Many crown counting approaches have been proposed in the recent years. In this paper we compare five deep-learning-based approaches to crowd counting, reevaluate them and present a novel CSRNet-based approach. We base our implementation on five convolutional neural network (CNN) architectures: CSRNet, Bayesian Crowd Counting, DMCount, SFA-Net, and SGA-Net and present a novel approach by upgrading CSRNet with application of a Bayesian crowd counting loss function and pixel modeling. The models are trained and evaluated on three widely used crowd image datasets, ShanghaiTech part A, part B, and UCF-QNRF. The results show that models based on SFA-Net and DM-Count outperform state-of-the-art when trained and evaluated on the similar data, and the proposed extended model outperforms the base model with the same backbone when trained and evaluated on the significantly different data, suggesting improved robustness levels.
Udaformer: Underwater Image Enhancement Based on Dual Attention Transformer

Underwater images suffer from seriously degraded due to wavelength-dependent light scatter and abortion of the underwater environment, which impacts the application of high-level computer vision tasks. Considering the characteristics of uneven degradation and loss of color channel of underwater images, a novel underwater image enhancement method, namely UDAformer, based on Dual Attention Transformer Block (DATB), including Channel Self-Attention Transformer (CSAT) and Shifted Window Pixel Self-Attention Transformer (SW-PSAT), is proposed. Specifically, the underwater image enhancement based on channel self-attention alone is not necessarily sufficient due to the severe and uneven degradation of underwater images. Therefore, a novel fusion method combining channel and pixel self-attention is proposed for efficient encoding and decoding of underwater image features. Then, the shifted window method for the pixel self-attention is proposed to improve computational efficiency. Further, convolution is constructed to calculate the self-attention weight matrix so that the proposed UDAformer can flexibly process input images of various resolutions and reduce network parameters. Finally, the underwater images are recovered through the design of skip connections based on the underwater imaging model. Experimental results demonstrate the proposed UDAformer surpasses previous state-of-the-art methods, both qualitatively and quantitatively.
A Model for the Adoption and Acceptance of Mobile Farming Platforms (MFPs) by Smallholder Farmers in Zimbabwe

The use of mobile technology has continued to rise in Zimbabwe, and farmers now can obtain agricultural information through the use of mobile technology. The potential of mobile phones in agriculture gave birth to mAgriculture, which is the use of mobile technology to provide agricultural information and services. Various technological start-up companies in Zimbabwe have developed mobile farming platforms as a means of increasing overall performance among smallholder farmers. Unfortunately, most of these mobile farming platforms have not achieved their much-anticipated impact on farmers due to low acceptance which can be attributed to the incompleteness of the available technology acceptance models. This paper contributes to the body of knowledge by proposing a more comprehensive model that extends the unified theory of acceptance and use of technology (UTAUT2) by incorporating two key factors which are attitude and cultural moderators. Structural Equation Modelling was utilized to test the hypothesized conceptual model, in a quantitative study conducted in Zimbabwe. As hypothesized, the findings of this study revealed that the constructs in the model were found to be significant determinants of behavioral intention and usage. The results also showed that cultural values have a moderating effect on user acceptance of mobile farming platforms.
Evaluating Presto and SparkSQL with TPC-DS

From the perspective of the development trend of database technology and the application of big data, the unified management and analysis of relational data and non-relational data is a new trend. New relational computing engines, such as SparkSQL and Presto, provide parallel processing and analysis of distributed relational data and non-relational data, which can effectively improve the performance of data analysis and data quality maintenance scenarios. The purpose of this work is to compare the performance of Presto and SparkSQL using TPC-DS as a benchmark to determine how well Presto and SparkSQL perform in the same scenario. TPC-DS is a benchmark test developed by the Transaction Processing Performance Council (TPC). It contains complex applications such as data statistics, report generation, online query, and data mining, and also has data skew and can effectively reflect system performance in real scenarios. In test results, Presto performed better than SparkSQL in many query scenarios, and in the most significant test results, Presto performed three times better than SparkSQL.
Cloud Computing – a Survey on Cloud Computing Platforms and Service Offerings

Now days cloud computing has become a scalable service for consumption and delivery platform in the modern information technology infrastructure. This scalable infrastructure bundle of all elements of the IT and network stack such as capable of development, delivery, and consumption of cloud services. It does effectively change the scenario of modern computation. With the advent of cloud computing, it is easy to manage resources like hardware, software, or storage. Mainly this technology aims at the service-oriented approach and focuses on cost reduction, hardware reduction, and pay just for services that have been subscribed. It helps to get better at the problems of data loss, accessing data whenever needed, and data security.
A Robust Method for Constructing Rotational Invariant Descriptors

This paper proposes a novel method for local feature description. Unlike most descriptors only encode the spatial information of one neighborhood of sample point, we propose a novel method to encode more than one local feature for each sample point. Based on our proposed method, two local descriptors: MIOP (Multi-neighborhood Intensity Order Pattern) and MIROP (Multi-neighborhood Intensity Relative Order Pattern) are presented in the paper. Thanks to the rotation invariant coordinate system, our proposed descriptors are built without reference orientation estimation. To evaluate the performance our descriptors and other existing descriptors (e.g. SIFT, LIOP, DAISY, HRI-CSLTP, MROGH), image matching experiments and object recognition experiments were conducted on Oxford dataset and additional image pairs with drastic illumination changes. The experimental results show that our proposed local descriptors exhibit better performance than other evaluated descriptors.