Reviewerly/exHarmony huggingface dataset:
Characterization of multimode linear optical networks

Multi-mode optical interferometers represent the most viable platforms for the successful implementation of several quantum information schemes that take advantage of optical processing. Examples range from quantum communication, sensing and computation, including optical neural networks, optical reservoir computing or simulation of complex physical systems. The realization of such routines requires high levels of control and tunability of the parameters that define the operations carried out by the device. This requirement becomes particularly crucial in light of recent technological improvements in integrated photonic technologies, which enable the implementation of progressively larger circuits embedding a considerable amount of tunable parameters. In this work, we formulate efficient procedures for the characterization of optical circuits in the presence of imperfections that typically occur in physical experiments, such as unbalanced losses and phase instabilities in the input and output collection stages. The algorithm aims at reconstructing the transfer matrix that represents the optical interferometer without making any strong assumptions about its internal structure and encoding. We show the viability of this approach in an experimentally relevant scenario, defined by a tunable integrated photonic circuit, and we demonstrate the effectiveness and robustness of our method. Our findings can find application in a wide range of optical setups, based both on bulk and integrated configurations.
Blockchain for Software Maintainability in Healthcare

In the healthcare industry, software quality plays a crucial role as it is used in almost all systems used for medical intervention. If the quality of healthcare software is not considered, it can potentially have a negative effect and may create catastrophic situations for the patient’s health and can seriously affect the organisation’s reputation. Maintainability is a key component of software quality, making up 80% of the total cost of the software development life cycle cost. Software maintainability can be assessed using metrics that are stored in a centralised location. Software maintainability metrics and related data are crucial. If attackers alter this data, it affects the overall quality of healthcare software. In order to solve this issue, we adapted blockchain-based storage, which offers the advantages of decentralisation, immutability, and transparency and is a promising solution. We used Storj, a blockchain-based decentralised file system, to secure the maintainability metrics and related data. According to the results, the maintainability metrics data stored in the blockchain-based decentralised file storage is more secure, redundant, and cost-effective than centralised storage.
Image and Texture Independent Deep Learning Noise Estimation Using Multiple Frames

In this study, a novel multiple frame based image and texture independent Convolutional Neural Network (CNN) noise estimator is introduced. Noise estimation is a crucial step for denoising algorithms, especially for ones that are called “non-blind”. The estimator works for additive Gaussian noise for varying noise levels. The noise levels studied in this work have a standard deviation equal to 5 to 25 increasing 5 by 5. Since there is no database for noisy multiple images to train and validate the network, two frames of synthetic noisy images with a variety of noise levels are created by adding Additive White Gaussian Noise (AWGN) to each clean image. The proposed method is applied on the most popular gray level images besides the color image databases such as Kodak, McMaster, BSDS500 in order to compare the results with the other works. Image databases comprise indoor and outdoor scenes that have fine details and richer texture. The estimator has an accuracy rate of 99 % for the classification and favourable results for the regression. The proposed method outperforms traditional methods in most cases. And the regression output can be used with any non-blind denoising method.
Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation

Unsupervised domain adaptation (UDA) is an approach to minimizing the domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images, which improves the performance of the downstream tasks, for example, cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this letter, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL) and depth cycle consistency loss (DCCL) are used to bring depth information into the generative model. Experimental results show that DRDG reaches state-of-the-art accuracy between generative methods in cross-domain semantic segmentation tasks. Source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/miemieyanga/ResiDualGAN-DRDG</uri> .
Examining Digital Forensic Evidence for Android Applications

The examination of digital forensic evidence is a science highlighting the main areas of progress in forensic science. Various social media sites (SNS) providing e-mail services, messages, pictures, and videos have brought about a huge explosion in development. In recent times, Digital Forensics has expanded to be used in all institutions and companies, especially financial companies, pharmaceutical companies, and investment companies. With this electronic development, criminal activities have dramatically increased to obtain and steal data for personal or international interests or the so-called data theft. Therefore, the biggest challenge lies in protecting this information from theft and searching for digital forensic evidence so that the digital evidence is correct and sound from a forensic point of view. Against this, this paper provides a detailed review of the most important Android applications in digital forensics to attain, retrieve, and compare information altogether.
Qubit readout and quantum sensing with pulses of quantum radiation

Different hypotheses about a quantum system such as the logical state of a qubit or the value of physical interaction parameters can be investigated by the interaction with a probe field. Such fields may be prepared in particularly sensitive quantum states, and we can use quantum trajectories to model the stochastic measurement record and conditional evolution of the state of the quantum system subject to its interaction with a travelling pulse of radiation. Our analysis applies to different measurement strategies and to arbitrary input quantum states of the probe field pulse and it thus permits direct comparison of their metrological advantages.
Panel Segmentation: A Python Package for Automated Solar Array Metadata Extraction Using Satellite Imagery

The National Renewable Energy Laboratory (NREL) Python panel-segmentation package is a toolkit that automates the process of extracting accurate and valuable metadata related to solar array installations, using publicly available Google Maps satellite imagery. Previously published work includes automated azimuth estimation for individual solar installations in satellite images [1]. Our continued research focuses on automated detection and classification of solar installation mounting configuration (tracking or fixed-tilt; rooftop, ground, or carport). Specifically, a faster-region-based convolutional neural network Resnet-50 feature pyramid network model was trained and validated on 862 manually labeled satellite images. This model was used to perform object detection on satellite imagery, locating and classifying individual solar installations' mounting configuration and type. Model results showed a mean average precision score of 77.79%, with the model strongest at detecting fixed-tilt ground mount and fixed-tilt carport installations. The object detection model and its outputs have been incorporated into the panel-segmentation package's automated metadata extraction pipeline, which returns the mounting configuration and azimuth for individual solar arrays in satellite imagery [2]. The complete image dataset with labels has been released on the U.S. Department of Energy (DOE) DuraMAT DataHub, to encourage further research in this area [3].
Distribution of heavy metal (Pb, Cd and Hg) concentrations in sediment of Bone River, Gorontalo

The concentration of three heavy metals Pb, Cd and Hg ware measured in Bone River, Gorontalo sediment. The heavy metal pollution on sediment in Bone River have not been explored longitudinally. The samples were taken in July 2021 at 8 stations. The trend of metals ware observed in sediment as PbCdHg. The level of studied metals Cd and Hg did not exceed the safe limit by ANZECC, OSQG LEL and CCME TEL meanwhile Pb was met the limit by by ANZECC. However, the investigated showed that fluctuations due to differences in the characteristics of each location. Contamination factor (CF) and geoaccumulation index (Igeo) demonstrated that the sediment samples were low contamination and lightly polluted. Contamination by heavy metals in the Bone River implies that the conditions are much frightening for the biota and residents around the river.
An adaptive multi-objective particle swarm optimisation algorithm based on fitness distance to streamline repository

In recent years, multi-objective particle swarm optimisation (MOPSO) algorithm has been paid more attention. One of its indispensable structures is the maintenance and update mechanism of the repository. The existing mechanisms are relatively simple, and most of them are based on the crowding distance sorting strategy, and not conducive to the distribution and accuracy of the algorithms. The paper innovated this mechanism and proposed an adaptive multi-objective particle swarm optimisation algorithm to streamline repository based on fitness distance (FDMOPSO). Both the concept of fitness distance and the corresponding improve methods of mutation mechanism and adaptive mechanism was proposed. The algorithm itself was tested using benchmarks. The results show that the proposed application of fitness distance had a better improvement on the convergence and distribution. Compared with other algorithms, the FDMOPSO algorithm had the best overall performance.
Digital Uses of Students and College Success

While, at an educational level, the supposedly innovative nature of digital tools is the subject of passionate debate among teachers, the question of enhancing learning is raised less frequently. To capture this variable, this research was carried out in the first semester of 2020. The objective was to identify the place that digital technology occupies in the way university students study and to measure the effects of certain uses of digital technology on exam results. The quantitative approach and the field design were applied. Data were collected through a survey conducted among 325 volunteer Peruvian university students enrolled in three teacher training specialties: Social Sciences, Mathematics, and Language and Literature. The analyzes show that students make little use of digital tools to study in-depth and that when they use them, it is for instrumental purposes. Likewise, the statistical models reveal the absence of a significant effect of the student body’s digital activities on the exams’ results.
Importance of Cybersecurity in The Higher Education Sector 2022

Today, we are witnessing the widespread of the digital revolution and its applications in all fields, as the means of networking provided the exchange of information, experiences, and knowledge in all sectors, but with all these positive features, we find that today’s digital world has brought a wide range of risks of its own, such as attacks and malicious programs like viruses, worms, spyware files, etc. and the education sector higher education is not far from these negative influences that hinder the education process and affect its infrastructure and its periphery, including students, teachers, administrative assistants, the environment and teaching methods. For this risk, we need to awareness and cyber security training and adopt a solid cyber security policy in higher education sectors institution in order to protect this vital sector. This research paper will explore the importance of cybersecurity in this sector and provide the strategies that students, faculty, and staff can utilize to promote cybersecurity across higher education institutions.
The interaction of actor-independent and actor-dependent factors in new venture formation: The case of blockchain-enabled entrepreneurial firms

Abstract The study seeks to explore how blockchain technology enables the creation of new ideas for ventures and to examine the activities of founders and entrepreneurial teams in shaping those ideas. We adopted several theoretical frameworks – external enablers theory, dynamic capabilities (DCs), and dynamic managerial capabilities (DMCs) – to explain the interaction of the actor-independent and actor-dependent factors in the process of new firm formation. We analysed four Hungarian blockchain start-ups that operate across financial services, cryptocurrency trading, crypto asset management, energy, information technology, and identity industries and create high value-added and cross-industrial offerings for Hungarian and foreign markets. Using qualitative study research results, the study develops the model of external enablers, founders' and firm capabilities and new venture creation. We identify three interconnected external enablers – namely, market volatility associated with the growing popularity of cryptocurrencies and the underlying blockchain technology, the properties of blockchain, and the ideology behind the technology – and discuss the role of entrepreneurs' DMCs and sensing and seizing activities in discovering and shaping these enablers into profitable business ideas.
Using neural networks to create a reliable phase quality map for phase unwrapping

Two-dimensional phase unwrapping is a crucial step in interferometric signal processing. A phase quality map can help the unwrapping algorithm deal with low-quality and fast-changing regions. However, because existing algorithms cannot calculate a quality map representing the gradient quality directly, it is usually necessary to approximate the gradient quality with phase quality to assist the network-based phase unwrapping algorithm. Furthermore, they cannot withstand intense noise in low-quality regions, resulting in many errors in path-based algorithms. To address the aforementioned issues, this paper analyzes the essence of a quality map and proposes a quality map generation method based on a convolutional neural network. The generated quality maps are a pair, each indicating the quality of horizontal and vertical gradients. Experiments show that the quality map generated by this method can help path-based and network-based algorithms perform better.
Two-stage multiple object detection using CNN and correlative filter for accuracy improvement

Recent advancements in tracking methods, particularly correlative-filter-based methods, can be utilized to support object detection to achieve the desired accuracy and speed. This is especially beneficial to aid a weak object detection model that results from the transfer learning concept trained on limited datasets, thus significantly reducing accuracy. Therefore, the limitation that comes from small datasets and training facilities needs to be tackled by a novel approach that is not restricted by computation time. The results of the proposed method (two-stage detection) are documented here. The first stage employs a convolutional neural network-based multi-object detector algorithm, and the results with low confidence scores are fed into the second stage for confirmation. A correlative filter is applied in the second stage; thus, low-confidence score results from the object detector algorithm are checked if the appearance is similar to the target object’s model appearance on the previous frame. Accuracy measurements were performed for both stages with different metrics to suit each algorithm goal. Image processing using CNN-based object detection yields bounding boxes with corresponding confidence scores, which show the probability of the object being detected as belonging to the target object class. The correlation filter tracking stage results are quantified using the novel movement distance score that will be explained. While the correlative filter plays a role in reducing false negatives by increasing detectability, the movement distance score ensures that the added detectability should not introduce more incorrect object tracking results. By applying the combination in the form of this two-stage method, a 10% enhancement in detectability (compared to a standalone object detection method) was observed in the various testing scenarios.
Reorienting Library Space and Services of Book Depositories under the Conditions of Martial Law in Ukraine: The Case of the Largest Libraries of Zakarpattia

Objective. The research aims to explore the practical experience of the activity transformation of Ukrainian libraries during the military invasion of the russian federation on the territory of Ukraine. Methods. The analysis of world publications allowed: to attempt identifying the specific methods of library work during extreme events; to conduct an analysis of the services of educational and public libraries; to prove the effectiveness of psychological and moral library support to the population; and to identify its most effective forms. Results. The substantiation of the obtained results is based on the characteristics of the library of the Uzhhorod National University and the Transcarpathian Regional Universal Scientific Library named after F. Potushniak, aimed at supporting victims of war. The most active methods of library work during the first months of the war between russia and Ukraine were revealed. Analysis of the provision of library services confirms the importance of book depositories as an important centre for uniting people for mutual support. The paper outlines practical recommendations regarding the active participation of libraries in the lives of people forced to flee from places of active hostilities. There is a burst of creative library changes associated with the reorientation of the library space and services according to the social challenge posed by russia's military aggression. Conclusions. The ability of the largest library institutions in Zakarpattia to provide moral support to people affected by the war has been revealed. The role of library support as an important factor in practical assistance to people has been confirmed.
Oversampling, Undersampling, Smote SVM dan Random Forest pada Klasifikasi Penerima Bidikmisi Sejawa Timur Tahun 2017

Bidikmisi is tuition assistance from the government for high school graduates (SMA) or equivalent who have good academic potential but have economic limitations. Different from scholarships that focus on providing awards or financial support to those who excel. The achievement requirements for Bidikmisi are aimed at ensuring that Bidikmisi recipients are selected from those who truly have the potential and willingness to complete higher education. Given that the recipients of this bidikmisi must really be the right person, in this study a classification of the recipients of the 2017 bidikmisi in East Java will be carried out, in this study there is data that is not balanced the "Accepted" class is more than the "Not accepted" class. If the data is not balanced, almost all classification algorithms will produce much higher accuracy for the majority class than for the minority class. Researchers will handle class imbalances. The resampling technique used in research related to the prediction of bidikmisi recipients includes resampling techniques, namely Oversampling, Undersampling and SMOTE using two classification methods, namely SVM and Random Forest. The Oversampling technique was chosen because it does not reduce the amount of data but adds to the dataset that is lacking in the minority class. The Oversampling algorithm used is Synthetic Minority Over-sampling Technique (SMOTE), this algorithm was chosen from several resampling algorithms because SMOTE produces good accuracy and is effective in dealing with unbalanced classes because it reduces overfitting.
Question Answering System Using Semantic Reasoning on Ontology for The History of The Sumedang Larang Kingdom

Studying history can train us to understand the sequence of events and increase a sense of nationalism in the younger generation. However, today's young generation views studying history as boring and unimportant. Studying history is considered boring because it has the stereotype of having to learn by reading long writings in books. Therefore, in this study, a Question Answering System (QAS) was built using an ontology to get historical information and get to know the culture. With QAS, users don't have to read long sentences and spend a lot of time searching for historical information, users can also ask questions in natural language without having to pay attention to sentence structure. The ontology was chosen to be able to build a knowledge base on the historical domain and SPARQL was used to find answers in the ontology. The construction of this system is expected not only to help introduce the history of the Sumedang Larang Kingdom but also to be able to introduce the attraction of cultural tourism in Indonesia, especially the Sumedang Larang Kingdom. The results of the evaluation with the system accuracy test showed a result of 87%.
Impact of Instruction-based LEGO WeDo 2.0 Robotic and Hypermedia on Students’ Intrinsic Motivation to Learn Science

Robots and hypermedia have been used as effective technological tools in the educational field. Despite their educational benefits, there are no studies investigating their impact on primary students’ intrinsic motivation in the field of science. For these reasons, the study aims to recognize the impact of employing LEGO WeDo 2.0 robotic and hypermedia on intrinsic motivation to learn science among primary students. The study implemented the quasi-experimental approach with a dual group design. The experimental group (n=25) was instructed on the force and motion topic using instruction-based LEGO WeDo 2.0 robotic and hypermedia, while the control group (n=25) was instructed using traditional instruction. The study conducted in Jordan involved fifty primary students. The data was gathered by administering the developed Intrinsic Motivation Towards Learning Science Scale to both groups at pre and post-points; ANCOVA analysis revealed a significant effect in favor of the experimental group in increasing intrinsic motivation to learn science at α = 0.05. These results recommend science instructors employ robots and hypermedia to foster students’ intrinsic motivation. It also encourages decision-makers in educational institutions to make decisions related to the science curriculum and its instructional methods.