Reviewerly/exHarmony huggingface dataset:
Fraud Prevention in Taxation System of Pakistan Using Blockchain Technology

Taxation department of Pakistan collects and keeps record of legislated taxes imposed by governments.Pakistan is among those countries which still maintain records of taxations in registers, thus maintaining immutability of records is really hard.Tax records have great risk of being lost or altered by someone and results in leading towards committing fraud in taxes.Blockchain has provided advantage to many industries because of its enhanced security and traceability in current trustless environment.We have presented a tax management solution which has an objective to provide secure, efficient and auditable system in Pakistan environment using blockchain technology.Our proposed methodology is about digitizing of the records of taxations using the blockchain distributed ledger based technology.Finally, future work prediction is also given, which will help getting directions for controlling fraud cases from most senior officers posts.
Image-to-Graph Convolutional Network for Deformable Shape Reconstruction from a Single Projection Image

Shape reconstruction of deformable organs from two-dimensional X-ray images is a key technology for image-guided intervention. In this paper, we propose an image-to-graph convolutional network (IGCN) for deformable shape reconstruction from a single-viewpoint projection image. The IGCN learns relationship between shape/deformation variability and the deep image features based on a deformation mapping scheme. In experiments targeted to the respiratory motion of abdominal organs, we confirmed the proposed framework with a regularized loss function can reconstruct liver shapes from a single digitally reconstructed radiograph with a mean distance error of \(3.6 \text { mm}\).
Towards Time-Optimal Tunnel-Following for Quadrotors

Minimum-time navigation within constrained and dynamic environments is of special relevance in robotics. Seeking time-optimality, while guaranteeing the integrity of time-varying spatial bounds, is an appealing trade-off for agile vehicles, such as quadrotors. State-of-the-art approaches, either assume bounds to be static and generate time-optimal trajectories offline, or compromise time-optimality for constraint satisfaction. Leveraging nonlinear model predictive control and a path parametric reformulation of the quadrotor model, we present a real-time control that approximates time-optimal behavior and remains within dynamic corridors. The efficacy of the approach is evaluated by simulated results, showing itself capable of performing extremely aggressive maneuvers as well as stop-and-go and backward motions. Video: https://youtu.be/Apc8MCu7Yvo
Ion–photonic frequency qubit correlations for quantum networks

Efficiently scaling quantum networks to long ranges requires local processing nodes to perform basic computation and communication tasks. Trapped ions have demonstrated all the properties required for the construction of such a node, storing quantum information for up to 12 min, implementing deterministic high fidelity logic operations on one and two qubits, and ion–photon coupling. While most ions suitable for quantum computing emit photons in visible to near ultraviolet (UV) frequency ranges poorly suited to long-distance fibre optical based networking, recent experiments in frequency conversion provide a technological solution by shifting the photons to frequencies in the telecom band with lower attenuation for fused silica fibres. Encoding qubits in frequency rather than polarization makes them more robust against decoherence from thermal or mechanical noise due to the conservation of energy. To date, ion–photonic frequency qubit entanglement has not been directly shown. Here we demonstrate a frequency encoding ion–photon entanglement protocol in 171Yb+ with correlations equivalent to 92.4(8)% fidelity using a purpose-built UV hyperfine spectrometer. The same robustness against decoherence precludes our passive optical setup from rotating photonic qubits to unconditionally demonstrate entanglement, however it is sufficient to allow us to benchmark the quality of ion–UV photon correlations prior to frequency conversion to the telecom band.
Simulations and Experiments of the Soil Temperature Distribution after 2.45-GHz Short-Time Term Microwave Treatment

Microwave treatment is a green and pollution-free soil disinfection method. The application of microwaves to disinfect soil before cultivation is highly important to increase crop yields and protect the ecological environment. The electromagnetic field is an important parameter influencing the soil temperature field in the process of microwave soil treatment, and the change in soil temperature directly affects soil disinfection. Therefore, this article carried out research on the heating pattern in North China loess due to microwave treatment. First, COMSOL software was employed to simulate the microwave soil treatment process to analyze microwave penetration into soil. Second, with the application of microwaves at the designed frequency produced with a 2.45-GHz tunable microwave generating microdevice, soil with water contents of 0%, 10%, 20%, and 30% was treated for 10~60 s (at 10-s time intervals), and experiments on the influence of the microwave output power, treatment time, and soil moisture content on the soil temperature were performed via the controlled variable method. The simulation results indicate that with increasing soil moisture content, the microwave frequency inside the soil model increases, and the electric field intensity value decreases in the model at the same depth. After microwaves traverse through the 20-cm soil model, the incident field strength is three orders of magnitude lower than the outgoing field strength. The results of the microwave soil treatment experiment reveal that: (1) Compared to microwave output power levels of 1.8 and 1.6 kW, a level of 2 kW is more suitable for microwave soil disinfection. (2) After treatment, the highest temperature occurs on the soil surface, not within the soil. (3) The location of the highest soil internal temperature after microwave treatment increasingly approaches the soil surface with increasing soil moisture content, and the microwave output power does not affect the location of the highest soil internal temperature. Combining the electromagnetic field simulation and microwave soil treatment experiment results, it was found that the higher the field strength is, the higher the temperature value, and the highest soil internal temperature after microwave treatment often occurs at the first electromagnetic wave peak.
Electronics Student Worksheet Based on Higher Order Thinking Skills for Grade IV Elementary School

The Student Worksheets (LKPD) available so far only contain black and white writing. In addition, the pictures and questions presented did not attract students' interest in learning. Therefore, it is necessary to develop LKPD that can attract students' learning interests. This study aims to develop Electronic LKPD (E-LKPD) thematic learning based on Higher Order Thinking Skills (HOTS). This research is a type of development research (RnD). The development model uses the ADDIE model which consists of the Analysis, Design, Development, Implementation, Evaluation stages. The E-LKPD product that has been developed is then tested on validators, teachers, and students to determine the effectiveness of its use. Data analysis techniques were carried out descriptively, qualitatively, and quantitatively. Validation is carried out by three validators, namely content, material, and language validators. The research instrument is a teacher response questionnaire for practicality and a student response questionnaire for attractiveness. The average validation result of media experts is 3.6 with the "very valid" category, while the material expert is 3.83 with the "very valid" category and the linguist is 2.83 in the "valid" category with revisions. The results of the analysis of the practicality of the E-LKPD can be assessed from the results of the teacher's questionnaire score obtained a score of 3.43 in the "very practical" category while the results of the analysis of the attractiveness of the E-LKPD obtained from the results of the student questionnaire score with a score of 3.19 in the "good" category. Based on the trial, it was concluded that the results of developing the E-LKPD product for HOTS-based thematic learning were valid, practical, and interesting to use in learning in grade IV elementary schools.
Image Denoising: Issues and Challenges

Extracting noise coefficients from images to make them more informative is of critical importance in plenty of applications. There have been a lot of denoising techniques existing in the literature. However, the fidelity of the denoising algorithm is determined by the quality of the reconstructed image. Minimum artifacts and better preservation of geometrical details such as edges and texture reflect efficient image reconstruction. This paper presents a review of some remarkable trends in the field of denoising. The foremost objective of this study is to provide an understanding of research in the context of detail preserving denoising. In this paper, we also explore how denoising challenges force growth of denoising techniques from spatial domain filters to transform domain, from transform domain to hybrid domain and then to dictionary learning based methods. While all these approaches aim at productive denoising, they possess different assumptions, advantages, and shortcomings. This paper also provides us a deep understanding of which method to be adopted to obtain the most reliable results.
ON THE INTRODUCTION OF E-LEARNING PORTFOLIO IN THE EDUCATIONAL PROCESS

This article discusses the essence and content, structural components, advantages of the electronic learning portfolio, which are considered effective pedagogical methods in modern education. The article also shows the results of the introduction of electronic portfolio in the educational process.
PENGEMBANGAN E-LKPD BERBASIS PENDEKATAN SAINTIFIK UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA PEMBELAJARAN IPA KELAS V SEKOLAH DASAR

This study aims to find out the characteristics of e-LKPD in the subjects of natural science (IPA) based on the scientific approach, the validation, and the effectiveness of the developed e-LKPD. This research used Research and Development (R&amp;D) method. In collecting the data, the researcher used questionnaires, observation, interviews, the result of the learning process, and documentation. The data analysis used descriptive statistics analysis. Development of e-LKPD includes 9 stages of development. The subject testing was conducted on 24 students of fifth-grade students of SD N 2 Surokarsan Yogyakarta. The results of this study are the characteristics of e-LKPD in the subjects of natural science (IPA) based on a scientific approach using 5M components. The validation of e-LKPD product of the expert validator material result is obtained an overall average of 3.34 in the very good category, the validation of language expert has obtained an overall average of 3.25 in the very good category, the validation of media expert has obtained an overall average of 3.66 in the very good category, and the validation of the teacher of fifth-grade is obtained an overall average of 3.83 in the very good category. The practicality of the e-LKPD product showed a positive response with an average percentage of 81,1% and 82,3%. The effectiveness of e-LKPD is measured by the student result of the learning process who gets a score of 82,81.
Solar Irradiation Forecasting by Long-Short Term Memory Using Different Training Algorithms

Deep learning nowadays is the hottest research topic with wide applications in various areas classifications, pattern recognition, solar forecasting, wind forecasting, etc. This paper presents the forecasting of one step ahead solar global horizontal irradiance (GHI) by long-short term memory (LSTM) deep learning network using three different training algorithms. The LSTM network is employed using three different training algorithms: adaptive moment estimation with momentum (ADAM), stochastic gradient descent with momentum (SGDM), and root mean square propagation (RMSprop). The 1 year of dataset is used for the training of the model, whereas monthly data is used to test the model. The two popular error metrics: root mean square error (RMSE) and mean absolute percentage error (MAPE) is considered to obtain the forecasting errors for each training algorithms-based LSTM model. The results prove that the LSTM with ADAM training algorithm outperforms the SGDM and RMSprop algorithm. The ADAM-based LSTM obtained the annual average RMSE of 68.62 (w/m2) and MAPE of 10.30%.
Convolutional Neural Network Compression through Generalized Kronecker Product Decomposition

Modern Convolutional Neural Network (CNN) architectures, despite their superiority in solving various problems, are generally too large to be deployed on resource constrained edge devices. In this paper, we reduce memory usage and floating-point operations required by convolutional layers in CNNs. We compress these layers by generalizing the Kronecker Product Decomposition to apply to multidimensional tensors, leading to the Generalized Kronecker Product Decomposition (GKPD). Our approach yields a plug-and-play module that can be used as a drop-in replacement for any convolutional layer. Experimental results for image classification on CIFAR-10 and ImageNet datasets using ResNet, MobileNetv2 and SeNet architectures substantiate the effectiveness of our proposed approach. We find that GKPD outperforms state-of-the-art decomposition methods including Tensor-Train and Tensor-Ring as well as other relevant compression methods such as pruning and knowledge distillation.
An Empirical Study on How Well Do COVID-19 Information Dashboards Service Users’ Information Needs

The ongoing COVID-19 pandemic highlights the importance of dashboards for providing critical real-time information.In order to enable people to obtain information in time and to understand complex statistical data, many developers have designed and implemented public-oriented COVID-19 "information dashboards" during the pandemic.However, development often takes a long time and developers are not clear about many people's information needs, resulting in gaps between information needs and supplies.According to our empirical study and observations with popular developed COVID-19 dashboards, this seriously impedes information acquirement.Our study compares people's needs on Twitter with existing information suppliers.We determine that despite the COVID-19 information that is currently on existing dashboards, people are also interested in the relationship between COVID-19 and other viruses, the origin of COVID-19, vaccine development, fake new about COVID-19, impact on women, impact on school/university, and impact on business.Most of these have not yet been well addressed.We also summarise the visualization and interaction patterns commonly applied in dashboards, finding key patterns between data and visualization as well as visualization and interaction.Our findings can help developers to better optimize their dashboard to meet people's needs and make improvements to future crisis management dashboard development.
Guidelines for Teacher Training in Mobile Augmented Reality Games: Hearing the Teachers’ Voices

Mobile augmented reality games (MARGs) can be leveraged for educational purposes, as there are several examples in the literature revealing their educational value. The supporting technologies for MARGs’ implementation in education are increasingly pervasive and popular, so it is time for their adoption in teacher practices. However, the integration of new practices in schools, with an impact on students’ learning, requires teacher training. For that, a 50 h workshop was con-ducted to promote the collaborative development of MARGs for science, technology, engineering, and mathematics learning, which was attended by 16 in-service teachers, in Portugal. The aim of this study is to propose a set of guidelines for teacher training on MARGs, emergent from the perspectives of teachers who participated in the workshop. This is a descriptive qualitative study focusing on documental analysis of the individual final reflections of the participating teachers. The results showed that the workshop answered teachers’ personal and professional needs, with an impact on their practices, in what concerns the integration of mobile and AR technology, and of games. Producing a new game during training and making it freely available for others to use seemed to be very satisfactory for teachers, and a good practice to follow. From teachers’ reflections, it was possible to propose a set of guidelines useful for international readership, both researchers and teacher trainers, who aim to conceive and develop continuous professional development initiatives for MARGs’ integration in teacher practices.
Visual Analytics in Process Mining for Supporting Business Process Improvement

The increasing amounts of data have affected conceptual modeling as a research field. In this context, process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution. While automatic algorithms for process mining and analysis are needed to filter out irrelevant data and to produce preliminary results, visual inspection, domain knowledge, human judgment and creativity are needed for proper interpretation of the results. Moreover, a process discovery on an event log usually results in complicated process models not easily comprehensible by the business user. To this end, visual analytics has the potential to enhance process mining towards the direction of explainability, interpretability and trustworthiness in order to better support human decisions. In this paper we propose an approach for identifying bottlenecks in business processes by analyzing event logs and visualizing the results. In this way, we exploit visual analytics in the process mining context in order to provide explainable and interpretable analytics results for business processes without exposing to the user complex process models that are not easily comprehensible. The proposed approach was applied to a manufacturing business process and the results show that visual analytics in the context of process mining is capable of identifying bottlenecks and other performance-related issues and exposing them to the business user in an intuitive and non-intrusive way.
A Logical Characterization of the Preferred Models of Logic Programs with Ordered Disjunction

Logic Programs with Ordered Disjunction (LPODs) extend classical logic programs with the capability of expressing alternatives with decreasing degrees of preference in the heads of program rules. Despite the fact that the operational meaning of ordered disjunction is clear, there exists an important open issue regarding its semantics. In particular, there does not exist a purely model-theoretic approach for determining the most preferred models of an LPOD. At present, the selection of the most preferred models is performed using a technique that is not based exclusively on the models of the program and in certain cases produces counterintuitive results. We provide a novel, model-theoretic semantics for LPODs, which uses an additional truth value in order to identify the most preferred models of a program. We demonstrate that the proposed approach overcomes the shortcomings of the traditional semantics of LPODs. Moreover, the new approach can be used to define the semantics of a natural class of logic programs that can have both ordered and classical disjunctions in the heads of clauses. This allows programs that can express not only strict levels of preferences but also alternatives that are equally preferred. This work is under consideration for acceptance in TPLP.
Enhancing digital marketing performance through usage intention of AI-powered websites

&lt;p&gt;&lt;span&gt;Digital and wireless technology are a crucial part of today’s modern life. Artificial intelligence (AI) uses different technologies and systems for speech recognition, visual perception and decision making to mimic human functions. This study explores the impact of AI on website interactivity and the ease of use for enhancing digital marketing performance. The methodology used is qualitative with structured interviews, using three artificial intelligence-powered websites (Amazon, Alibaba, and Uber) as reference. The participants' structured interview responses were grouped into different thematic heading for coding and were subsequently analyzed by NVivo. The result found that artificial intelligence empowered websites were interactive, participants don’t feel safe and secure, easy to use, and tend to boost digital marketing performances. This study implies that more digital marketing companies should consider integrating artificial intelligence capabilities in their business operations. More security features should be embedded to help customers calm the fears of web insecurities.&lt;/span&gt;&lt;/p&gt;
Breast Cancer Detection and Diagnosis Using Machine Learning: A Survey

Breast cancer is one of the most widespread diseases causing death among women worldwide.Whenever a suspicion is raised, periodical exams usually including digital mammograms (DM), Infrared thermography, magnetic resonance imaging (MRI), ultrasound (US), microscopic (histological) images, microwave images, or other tools or tests might be recommended.Recently, many hardware and software have been applying different techniques for achieving high-quality results, especially the techniques of machine learning.In this paper, a comprehensive survey to review most of the accurate techniques being used for both detecting and diagnosing breast cancer is conducted.Besides, different commercial and non-commercial hardware and software are mentioned with their advantages and disadvantages in the process of detecting and diagnosing breast lesions.This study reveals that many techniques have been raised to help for breast cancer detection and diagnosis, however, there is no perfect modality that can detect and diagnose breast cancer alone.Moreover, a complete system that can deal with different modalities and gives 100% accuracy still a challenge, since the various structure of breast cancer and the different structure of images issued by a group of modalities that have been used.
Single-dataset Experts for Multi-dataset Question Answering

Many datasets have been created for training reading comprehension models, and a natural question is whether we can combine them to build models that (1) perform better on all of the training datasets and (2) generalize and transfer better to new datasets.Prior work has addressed this goal by training one network simultaneously on multiple datasets, which works well on average but is prone to over-or under-fitting different subdistributions and might transfer worse compared to source models with more overlap with the target dataset.Our approach is to model multi-dataset question answering with an ensemble of single-dataset experts, by training a collection of lightweight, dataset-specific adapter modules (Houlsby et al., 2019) that share an underlying Transformer model.We find that these Multi-Adapter Dataset Experts (MADE) outperform all our baselines in terms of in-distribution accuracy, and simple methods based on parameter-averaging lead to better zero-shot generalization and few-shot transfer performance, offering a strong and versatile starting point for building new reading comprehension systems. 1