Reviewerly/exHarmony huggingface dataset:
Blockchain based Security Solutions with IoT Application in Construction Industry

Abstract Blockchain Technology is an evolving digital technology that has become widely applicable in different industry and business sectors in recent years, particularly in financial and banking areas due to the rapid rise in the value of cryptocurrencies in recent years. A blockchain technology is basically a decentralized database that records any transaction made within the network, which is known as a ‘node,’ which includes encrypted data from the entire background of the transaction. In any sector, the implementation of decentralized technology lead to strengthening, protection, implementation of accountability and a transition from current centralized systems to a decentralized system, which is required in construction industry. The construction industry will continue to be a crucial engine of economic development for all nations. Construction industry is one of the world’s most important sectors. Application of Blockchain and IoT plays a very important role in construction industry. In construction industry, the decentralized ledger of data gathered from sources includes transactions and accounts via a linked network and relies on the agreement between the node points, which strengthens Blockchain’s transparency, traceability, and collaborative existence. This paper presents an overview and motivation for the application of IoT &amp; Blockchain Technology in the construction industry. The integration of Blockchain technology and IoT (Internet of Things) strengthens the whole system and makes real time more applicable. This paper therefore shows Blockchain’s potential applications with IoT in the infrastructure and construction industry.
The Genesy Model for a Blockchain-Based Fair Ecosystem of Genomic Data

Recent advances in technology have drastically downsized costs and implementation times for genomic services. The wide availability of low-cost genomic technologies and easy access to genomic data can significantly improve healthcare productivity and efficiency, all to the benefit of social well-being in general. For example, by creating the conditions for researchers to identify the causes of multiple diseases and contributing to the development of new drugs, we can improve the quality of life and give people, as users of genomic services, the means to positively impact their health. This article describes how blockchain technology can lay the foundations of an ecosystem that encourages users to acquire and share their genomic data in full awareness without fear of being circumvented to participate in the benefits and advances in genomic research. The starting point is Genesy , an innovative blockchain platform that transcribes genomic data, thus facilitating and, at the same time safeguarding, the users of genomic services in their relationships with parties interested in accessing and using data they own, such as research centers, pharmaceutical companies, hospitals, and geneticists. This result is obtained by exploiting blockchain technology's capabilities to notarize data and prevent their unauthorized use, and at the same time to make them objects of possible transactions between different parties. Looking ahead, the Genesy model can be generalized to promote an ecosystem, and a fair market, for all types of biomedical data.
Discovering Metamorphic Relations for Scientific Software From User Forums

Scientific software can be used for decades and is constantly evolving. Recently, metamorphic testing, a property-based testing technique, has shown to be effective in testing scientific software, and the necessary properties are expressed as metamorphic relations. However, the development of metamorphic relations is difficult: it requires considerable practical expertise for the software tester. In this article, we report our experience of uncovering metamorphic relations from a user forum's questions of the United States Environmental Protection Agency's Storm Water Management Model (SWMM). Our study not only illustrates a wealth of end users' expertise in interpreting software results, but also demonstrates the usefulness of classifying the user-oriented metamorphic relations into a nominal, ordinal, and functional hierarchy mainly from the software output perspective.
Catching the Drone - A Tangible Augmented Reality Game in Superhuman Sports

The newly defined genre of Superhuman Sports provides several challenges, including the need to develop rich Augmented Reality (AR) game concepts with tangible interactions and augmentation. In this paper, we provide insights into a Superhuman Sports ball game, where players are able to interact in mid-air, rapidly and precisely with a smart, augmented and catchable drone ball. We describe our core concepts and a path towards a fully functional system with multiple and potentially different display solutions, ranging from smartphone-based AR to, eventually, HMD-based AR. For this AR game idea, a unique drone with a trackable cage based on LED pattern recognition has been developed. The player, as well as the drone will move swiftly during the game. To precisely estimate the 6DoF pose of the fast-moving drone in this dynamic scenario, we propose a suitable pipeline with a tracking algorithm. As foundation for the tracking, LEDs have been placed in a specific, spherical pattern on the drone cage. Furthermore, refinements based on the unique attributes of LEDs are considered.
Ontology-Mediated SPARQL Query Answering over Knowledge Graphs

Ontology-Mediated Query Answering (OMQA) is a well-established framework to answer queries over Knowledge Graphs (KGs), enriched with rdfs or owl ontologies. OMQA was originally designed for Unions of Conjunctive Queries (UCQs), and based on certain answers. More recently, OMQA has been extended to sparql queries, but to our knowledge, none of the efforts made in this direction (either in the literature, or the so-called W3C sparql entailment regimes) is able to capture both certain answers for UCQs and the standard interpretation of sparql over a plain graph. We formalize these as requirements to be met by any semantics that aims at conciliating certain answers and sparql answers, and extend these with three additional requirements. Then we define two semantics that satisfies all requirements for sparql queries with select, union, join, and optional. Finally, we investigate the combined complexity of query answering under these semantics over a KG enriched with a DL-LiteR ontology, showing that for several fragments of sparql, known upper-bounds for query answering over a plain KG are matched.
Educating Farmers Using Participatory Rural Appraisal Construct

The Digital Based Community Economy Program has been created to expand market access and improve the quality of farmers. The program is called the “Online Farming Program”, in collaboration with the Ministry of Communication and Information Technology, as well as several digital business startups who have created mobile-based applications that aim to increase productivity in agriculture and advance farmers. Standard of living. However, the socialization of the Go-Online Farmers program, which is still not optimal for farmers who are not familiar with computer programs and are not familiar with technology is one of the obstacles they face. So in the era of the industrial revolution 4.0, the Participatory Rural Appraisal (PRA) and Media Production Concept (MPC) methods were used so that the output generated from this study could educate and add to the information obtained by farmers and help farmers buy and sell online so they could use the Go-Online program maximally. It can be concluded that from this research a strategy is needed to disseminate the Go-Online Farmer Program to farmers so that researchers implement a video motion graphic to facilitate this research which is uploaded to the YouTube channel so that information can be disseminated, readily accepted, and distributed to 88.813 villages in Indonesia.
Mixed Poisson Gaussian noise reduction in fluorescence microscopy images using modified structure of wavelet transform

Fluorescence microscopy is an important investigation tool of discoveries in the field of biological sciences where the imaging phenomena are limited by the noise. This paper introduces the integration of biorthogonal wavelet filters along with mixed Poisson-Gaussian unbiased risk estimate (MPGURE) based subband adaptive thresholding function for the restoration of low photon count microscopy images. The proposed algorithm consists of four steps. In the first step, variance stabilization transform along with a multi-scale Wiener filtering approach is used to filter out the noise and blurring effect. In the second step, deconvolved images are further decomposed by the biorthogonal wavelet filters. The modified wavelet subband structure is used for the identification of noisy and noise-free subbands. In the next stage, different noisy coefficients are thresholded using the MPGURE-based thresholding operation. The different thresholded images are combined along with different optimum coefficients. Finally, inverse variance stabilization transformation is applied to obtain the final restored output. Performance of the proposed algorithm is tested on 14 different benchmark image data sets with performance evaluation measures like signal-to-noise ratio, peak signal-to-noise ratio, mean structural similarity index measur, and correlation coefficient. Simulation results of the proposed algorithm claim better results than other state-of-the-art techniques.
3-D Block-Rooting Scheme With Application to Medical Image Enhancement

The goal of image enhancement is to improve certain features and details on an image. It is a critical procedure in image processing (image segmentation, feature extraction), medical imaging (such as X-ray, CT, and MRI) and computer vision (target, object, and text detection; segmentation, registration, and recognition). X-ray, CT, and MRI images are often affected by blurriness and lack of contrast, but the clarity of these images is very important for the accuracy of medical diagnosis and treatment. This article offers (1) a 3-D transform block-rooting scheme-based image enhancement method and (2) a non-reference transform-domain quality measure to choose the presented algorithm parameters optimally. Experimental results from NYU, fastMRI, and ChestX-ray data sets show that the proposed technique performs well and can reduce noise during the sharpening of the image details. At the same time, it can undoubtedly be used in different medical image processing systems.
Economic viability of rooftop photovoltaic systems in the middle east and northern African countries

Renewable energy sources and the possible sustainability that can be achieved through them has become a common discourse in the world today. The Middle East and Northern African countries have high potential to benefit from large scale renewable energy projects. These nations may initiate or permit the use of residential rooftop photovoltaic (PV) systems in the near future. However, data regarding system design, grid compatibility, and consumption of energy produced through rooftop photovoltaic systems is insufficient. The supporting policies and a feed-in tariff lack, thereby creating more research and development issues. Therefore, this research study aims to use the ten most populated nations in the Middle East and Northern Africa to (1) compare the use of rooftop PV systems in ten populated cities and (2) to evaluate the economic viability of rooftop PV systems under varying policies and electricity rate schemes. The findings of the study can help policymakers understand the application of rooftop PV systems in the Middle East and Northern African countries. The study, the first of its kind, uses an economic model to evaluate rooftop PV systems in ten identical houses in ten of the most populated cities in the Middle East and Northern African countries.
Carotid Artery Segmentation on Ultrasound Image using Deep Learning based on Non-Local Means-based Speckle Filtering

Cardiovascular disease (CVD) causes significant deaths worldwide, of which 17.3 million deaths per year are due to CVD. The use of Ultrasound is necessary to see the abnormalities. The study will segment Carotid Artery segmentation on the Ultrasound image by using the U-Net-based architecture of non-local means-based speckle filtering (NLMBSF). The images will use NLMBSF to reduce speckles, and the data set will be divided into two parts, namely the dataset, which using NLMBSF and not NLMBSF. After that, doing training to create a U-net model, the training data model results will be searched with the best Accuracy. The obtained result of the study is an accuracy value of 97.74%, dice value is 87.22%, and a loss of 0.0107 on data that does not use NLMBSF. Still, it got different data results using NLMBSF, namely 97.6% accuracy, dice value is 84.06% and 0.0138 value loss.
Deep Learning Ensembles for Hate Speech Detection

Our study explores offensive and hate speech detection for the Arabic language, as previous studies are minimal. Based on two-class, three-class, and six-class Arabic-Twitter datasets, we develop single and ensemble CNN and BiLSTM classifiers that we train with non-contextual (Fasttext-SkipGram) and contextual (Multilingual Bert and AraBert) word-embedding models. For each hate/offensive classification task, we conduct a battery of experiments to evaluate the performance of single and ensemble classifiers on testing datasets. The average-based ensemble approach was found to be the best performing, as it returned F-scores of 91%, 84%, and 80% for two-class, three-class and six-class prediction tasks, respectively. We also perform an error analysis of the best ensemble model for each task.
Constrained-optimization Approach Delivers Superior Classical Performance for Graph Partitioning via Quantum-ready Method

Graph partitioning is one of an important set of well-known compute-intense (NP-hard) graph problems that devolve to discrete constrained optimization. We sampled solutions to the problem via two different quantum-ready methods to understand the strengths and weaknesses of each method. First we formulated and sampled the problem as a quadratic unconstrained binary optimization (QUBO) problem, via the best known QUBO formulation, using a best-in-class QUBO sampler running purely classically. Second, we formulated the problem at a higher level, as a set of constraints and an objective function, and sampled it with a recently developed constrained-optimization sampler (which generates QUBOs and also samples them classically). We find that both approaches often deliver better partitions than the purpose-built classical graph partitioners. Further, we find that the constrained-optimization approach is often able to deliver better partitions in less time than the bespoke-QUBO approach, without specific knowledge of the graph-partitioning problem. Stepping back from graph partitioning itself, one key question is whether bespoke algorithms for high-value problems or general tools for classes of problems are more likely to deliver the power of QCs to a broad market of real-world users. We believe this early evidence, though anecdotal, supports the proposition that general tools may contribute significant benefit to a range of problems, expanding the impact of QCs on industry and society. The fact that this benefit is independent of the low-level sampler employed, whether classical software or one of a variety of QC architectures, reinforces the need for further work on high-level optimization. The commercial availability in the cloud of such software today, delivering superior classical performance for some problems, enables quantum-forward organizations to migrate to quantum-ready methods now, accelerating progress toward quantum advantage and ensuring sampler software evolves to address the problems such organizations value.
Efficient convex optimization-based texture mapping for large-scale 3D scene reconstruction

Texture mapping is a key step in large-scale 3D scene reconstruction, which can greatly enhance visual reality of the reconstructed scenes. However, existing techniques are unable to accomplish this task efficiently due to the high computational complexity of reconstructing large-scale real-world 3D scenes. In this work, we propose a new efficient convex optimization-based approach, i.e. the mesh-based continuous max-flow method, which can be easily implemented and accelerated upon a modern parallel computing platform, e.g. GPU. Particularly, an Markov Random Fields (MRF) based model, i.e. Potts model, is introduced to mathematically formulate the key specific view selection problem; we show that the challenging combinatorial optimization problem can be efficiently solved by resolving its convex relaxation, which recovers textures from images with the proposed duality-based continuous max-flow approach. In addition, visual effects of sharpness and deformation are utilized to define a criterion of evaluating texture quality effectively, and a large 3D triangular mesh is partitioned into structural components so as to reduce memory consumption of the proposed algorithm. The proposed mesh-based continuous max-flow approach for large-scale texture mapping demonstrates its outperformance over state-of-the-art methods, over large-scale public datasets, in both numerical efficiency and visual quality; meanwhile, our GPU-accelerated algorithm can yield 3D textured models with high quality from complex large-scale scenes in minutes.
AI-Based Techniques for Real-Time Face Recognition-based Attendance System- A comparative Study

Face recognition is a powerful tool for a biometric system that takes data from both images and videos. The traditional attendance system can be replaced by the automatic attendance system to utilize class time more effectively. In this paper real-time, attendance monitoring uses a web app that can be operated remotely by using a local server and Amazon Web Service (AWS) cloud recognition Application Programming Interface (API). The first approach follows five sections which are face detection, preprocessing, training and, face recognition through which attendance will be recorded and mailed to the respective teacher. The second approach is based on AWS recognition API which processes the data in the cloud.
Secure Storage and Access for Task-Scheduling Schemes on Consortium Blockchain and Interplanetary File System

Computerized systems and software, which allow optimizing and planning the processes of production, storage, transportation, sale, and distribution of goods, have emerged in the industry. Scheduling systems, in particular, are designed to control and optimize the manufacturing process. This tool can have a significant effect on the productivity of the industry because it reduces the time and cost through well-defined optimization algorithms. Recently, the applicability of blockchain technology has been demonstrated in scheduling systems to add decentralization, traceability, au-ditability, and verifiability of the immutable information that this technology provides. This is a novel contribution that provides scheduling systems with an additional layer of security. With the latest version of Hyperledger Fabric, the appropriate levels of permission and policies for access to information can be established with significant levels of privacy and security, which prevent malicious actors from trying to cheat or abuse the system. Different alternatives exist to manage all processes associated with the operation of a blockchain network, and among them, providers of blockchain as a service have emerged. Chainstack stands out for its simplicity and scalability features to deploy and operate a blockchain network. Our goal in this work is to create a solution for secure storage of and access to task-scheduling scheme on the consortium blockchain and inter-planetary file system as a proof of concept to demonstrate its potential and usability.
Adaptive δ-Generalized Labeled Multi-Bernoulli Filter for Multi-Object Detection and Tracking

The δ-generalized labeled multi-Bernoulli (δ-GLMB) filter is an efficient approach for multiobject tracking in case of high clutter density and low detection probability.However, the formulation of the original δ-GLMB filter requires that the birth δ-GLMB filtering density is known a priori.It is inapplicable for the birth object appearing from unknown positions.To address this problem, an adaptive δ-GLMB filter is proposed to detect and track the birth objects with unknown position information.This adaptive filter establishes the birth δ-GLMB filtering density by using measurements at previous three successive times.Simulation results indicate that the proposed adaptive δ-GLMB filter may efficiently detect and track the multiple objects with unknown positions.Simulation results also demonstrate that the proposed adaptive δ-GLMB filter performs better than the other existing adaptive filters.
Development and Evaluation of the H NMR MoleculAR Application

An augmented reality (AR) application and an activity worksheet have been developed to support students in visualizing the concepts involved when solving 1H NMR problems. This instructional resource was designed to encourage conceptual problem-solving and prevent memorization by eliminating the use of chemical shift tables. It uses the compare–predict–observe–explain (CPOE) cycle and contrasting cases to guide students to abstract principles about how proton equivalency, nuclear spin, electronegativity, and anisotropy impact the number, intensity, splitting, and shift of signals on a spectrum. The AR resource aids students in visualizing 3D structures, molecular orbitals, and electrostatic potential maps. Results from the user experience questionnaire revealed that students had a positive user experience and ranked the novelty of the application as its best feature. We also provide evidence using the Behavioral Observation of Students in Schools protocol that merits the use of this instructional resource in the laboratory setting on the basis of the minimal need for instructor’s assistance that was observed. This paper describes how the application H NMR MoleculAR, now freely available in Android and iOS stores, was incorporated as an in-lab app for the 1H NMR laboratory at our institution. It has also been administered remotely and has been proven to be a useful complement to organic chemistry instruction.
Explicit quaternion krawtchouk moment invariants for finger-spelling sign language recognition

Sign recognition is a difficult task due to the complexity of its composition which uses signs of different levels, words, facial expression, body posture and finger-spelling to convey meaning. With the development of recent technologies, such as Kinect sensor, new opportunities have emerged in the field of human computer interaction and sign language, allowing to capture both RGB and Depth (RGB-D) information. In the regard to feature extraction, the traditional methods process the RGB and Depth images independently. In this paper, we propose a robust static finger-spelling sign language recognition system adopting the Quaternion algebra that provide a more robust and holistical representation, based on fusing RGB images and Depth information simultaneously. Indeed, we propose, for the first time, a new sets of Quaternion Krawtchouk moments(QKMs) and Explicit Quaternion Krawtchouk Moment Invariants (EQKMIs). The proposed system is evaluated on three well-known finger-spelling datasets, demonstrate the performance of the novel method compared to other methods used in the literature, against geometrical distortion, noisy conditions and complex background, indicating that it could be highly effective for many other computer vision applications.