Reviewerly/exHarmony huggingface dataset:
Breast Cancer Detection Using Mammographic Images over Convolutional Neural Network

The biggest cause of death worldwide, particularly for women, is breast cancer. Early cancer detection and treatment can significantly increase the chances of survival and reduce costs for patients. The first important point of decision in cancer treatment is histology, which depends on a histologist confirming the presence of cancer. The time it takes to review the slides, the amount of data, and the potential for errors make these the main use cases of automatic detection in the health care setting. The ability to accurately and quickly identify potentially cancerous areas for review by histologists can and helps optimize the use of their time. This detection can be performed using CNN.MIAS dataset for mammograms is employed for this purpose, with 322 mammograms, almost all of which 189 photos of normal breasts and 133 photographs of aberrant breasts are shown. In this article, computer vision models like the convolutional neural network are used to predict whether mammary gland tissues are benign or cancerous. The investigation is ongoing, and new advancements are being made by enhancing the CNN architecture and utilising trained neural networks that, ideally, will result in more accurate measurements.
Network Security Analytics on the Cloud: Public vs. Private Case

Our networks, PCs, tablets, mobile phones, and other devices are exposed to security risks and attacks executed by cybercriminals on daily bases. The detection and prevention of cyber threats are done by IDS/IPS systems but they are not flexible enough when it comes to using threat models. The threat intelligence frameworks on the other hand typically require significant computational power. All these requirements can be fulfilled by contemporary cloud technologies, but in many cases, public clouds are not acceptable due to privacy, security, and efficiency concerns. This article presents an implementation of a framework for security analytics in the area of detection of unauthorized intrusions using the technology of the private cloud. It has many of the advantages of the big public clouds but fundamentally differs from them when it comes to data management, operation interoperability, and costs. It is suitable for small and medium data centers and large companies, which prefer to keep the data on their premises or to isolate the operations within managed servers on their private clouds hosted by public data centers.
A New Chapter for Medical Image Generation: The Stable Diffusion Method

Data collecting and sharing have been widely accepted and adopted to improve the performance of deep learning models in almost every field. Nevertheless, in the medical field, sharing the data of patients can raise several critical issues, such as privacy and security or even legal issues. Synthetic medical images have been proposed to overcome such challenges; these synthetic images are generated by learning the distribution of realistic medical images but completely different from them so that they can be shared and used across different medical institutions. Currently, the diffusion model (DM) has gained lots of attention due to its potential to generate realistic and high-resolution images, particularly outperforming generative adversarial networks (GANs) in many applications. The DM defines state of the art for various computer vision tasks such as image inpainting, class-conditional image synthesis, and others. However, the diffusion model is time and power consumption due to its large size. Therefore, this paper proposes a lightweight DM to synthesize the medical image; we use computer tomography (CT) scans for SARS-CoV-2 (Covid-19) as the training dataset. Then we do extensive simulations to show the performance of the proposed diffusion model in medical image generation, and then we explain the key component of the model.
A Secure Decentralized E-Voting with Blockchain &amp; Smart Contracts

A democratic election is a crucial act in each nation, as it determines the country's future for a specific term. Some of the older voting methods, such as Ballot Paper and EVM (Electronic Voting Machine), have disadvantages such as lack of transparency, poor voter turnout, vote rigging, and many others. Using Blockchain technology and Smart Contracts, it is simple to circumvent the flaws of the Ballot system and EVM. Electronic Voting Powered by Blockchain and Smart Contracts outperforms these antiquated voting methods by delivering secure results in less time and at a lower cost. With E-Voting utilizing Blockchain, prices can be lowered, the necessity for Polling stations and the consumption of resources such as EVMs and Ballot Papers may be decreased, and security can be improved by offering End-to-End Encryption and authenticity. This blockchain-powered e-voting can readily acquire trust due to the transaction's transparency, immutability, and difficulty of modification once hosted, as a result of smart contracts. Using OTP Verification and face verification, the suggested solution is a MERN-based web application with a multitude of upgraded authentication and permission techniques. To improve security, this voting data is saved as a transaction in a Blockchain-based distributed ledger using smart contracts.
Characterization of the photoelastic dispersion coefficient using polarized digital holography

The photoelastic dispersion coefficient represents the relationship between stress and the differences in refractive indices in a birefringent material. However, determining the coefficient using photoelasticity is challenging, as it is difficult to determine the refractive indices within photoelastic samples that are under tension. Here we present, for the first time, to our knowledge, the use of polarized digital holography to investigate the wavelength dependence of the dispersion coefficient in a photoelastic material. A digital method is proposed to analyze and correlate the differences in mean external stress with differences in mean phase. The results confirm the wavelength dependence of the dispersion coefficient, with an accuracy improvement of 25% compared to other photoelasticity methods.
Multi-Scopic Cognitive Memory System for Continuous Gesture Learning

With the advancement of artificial intelligence technologies in recent years, research on intelligent robots has progressed. Robots are required to understand human intentions and communicate more smoothly with humans. Since gestures can have a variety of meanings, gesture recognition is one of the essential issues in communication between robots and humans. In addition, robots need to learn new gestures as humans grow. Moreover, individual gestures vary. Because catastrophic forgetting occurs in training new data in traditional gesture recognition approaches, it is necessary to preserve the prepared data and combine it with further data to train the model from scratch. We propose a Multi-scopic Cognitive Memory System (MCMS) that mimics the lifelong learning process of humans and can continuously learn new gestures without forgetting previously learned gestures. The proposed system comprises a two-layer structure consisting of an episode memory layer and a semantic memory layer, with a topological map as its backbone. The system is designed with reference to conventional continuous learning systems in three ways: (i) using a dynamic architecture without setting the network size, (ii) adding regularization terms to constrain learning, and (iii) generating data from the network itself and performing relearning. The episode memory layer clusters the data and learns their spatiotemporal representation. The semantic memory layer generates a topological map based on task-related inputs and stores them as longer-term episode representations in the robot's memory. In addition, to alleviate catastrophic forgetting, the memory replay function can reinforce memories autonomously. The proposed system could mitigate catastrophic forgetting and perform continuous learning by using both machine learning benchmark datasets and real-world data compared to conventional methods.
Virtual Reality as the Catalyst for a Novel Partnership Model in Initial Teacher Education: ITE Subject Methods Tutors’ Perspectives on the Island of Ireland

This small-scale study explores the attitudes of fifty initial teacher education (ITE) subject methods tutors towards the use of virtual reality (VR) in education and considers whether VR could be a catalyst for reviewing the partnership model within ITE programmes. In addition, this study offers a novel solution to ITE tutors’ challenges when managing their own technological and pedagogical development alongside preparing student teachers for technology-enhanced learning (TEL). Building on previous research on ITE tutors’ use of TEL across the island of Ireland, this paper discusses the synergy between the cognitive apprenticeship model and reverse mentoring that upends the classic co-operating teacher/student teacher hierarchy and positions all partners in ITE programmes as learning leaders at key points in the partnership process. An online survey comprising 51 items was administered to a purposive sample of 50 ITE tutors from four ITE providers, two in Ireland and two in Northern Ireland (NI), who were selected due to their developing interest in the use of virtual reality in teacher education. Data analysis using SPSS combined with thematic analysis of open-ended responses revealed that although the majority of ITE subject methods tutors conveyed open-minded and willing attitudes to embrace VR in the future, they identified a number of systemic issues that need to be addressed first. These include the disconnect between innovative pedagogical practices presented in university-based modules and the stark reality of technological deficits in some classrooms; the pedagogical and resource-based ‘readiness’ of ITE tutors to embed VR in their subject-specific teaching; and the need for curriculum-focused, VR resources for school-based use. As a result of this study, an incremental, cyclical approach to growing the body of knowledge around VR pedagogy is proposed in conjunction with new forms of collaboration between the partners in ITE.
A Tracklet-before-Clustering Initialization Strategy Based on Hierarchical KLT Tracklet Association for Coherent Motion Filtering Enhancement

Coherent motions depict the individuals’ collective movements in widely existing moving crowds in physical, biological, and other systems. In recent years, similarity-based clustering algorithms, particularly the Coherent Filtering (CF) clustering approach, have accomplished wide-scale popularity and acceptance in the field of coherent motion detection. In this work, a tracklet-before-clustering initialization strategy is introduced to enhance coherent motion detection. Moreover, a Hierarchical Tracklet Association (HTA) algorithm is proposed to address the disconnected KLT tracklets problem of the input motion feature, thereby making proper trajectories repair to optimize the CF performance of the moving crowd clustering. The experimental results showed that the proposed method is effective and capable of extracting significant motion patterns taken from crowd scenes. Quantitative evaluation methods, such as Purity, Normalized Mutual Information Index (NMI), Rand Index (RI), and F-measure (Fm), were conducted on real-world data using a huge number of video clips. This work has established a key, initial step toward achieving rich pattern recognition.
Text Semantic Fusion Relation Graph Reasoning for Few-Shot Object Detection on Remote Sensing Images

Most object detection methods based on remote sensing images are generally dependent on a large amount of high-quality labeled training data. However, due to the slow acquisition cycle of remote sensing images and the difficulty in labeling, many types of data samples are scarce. This makes few-shot object detection an urgent and necessary research problem. In this paper, we introduce a remote sensing few-shot object detection method based on text semantic fusion relation graph reasoning (TSF-RGR), which learns various types of relationships from common sense knowledge in an end-to-end manner, thereby empowering the detector to reason over all classes. Specifically, based on the region proposals provided by the basic detection network, we first build a corpus containing a large number of text language descriptions, such as object attributes and relations, which are used to encode the corresponding common sense embeddings for each region. Then, graph structures are constructed between regions to propagate and learn key spatial and semantic relationships. Finally, a joint relation reasoning module is proposed to actively enhance the reliability and robustness of few-shot object feature representation by focusing on the degree of influence of different relations. Our TSF-RGR is lightweight and easy to expand, and it can incorporate any form of common sense information. Sufficient experiments show that the text information is introduced to deliver excellent performance gains for the baseline model. Compared with other few-shot detectors, the proposed method achieves state-of-the-art performance for different shot settings and obtains highly competitive results on two benchmark datasets (NWPU VHR-10 and DIOR).
Visualization of Environmental Sensing Data in the Lake-Oriented Digital Twin World: Poyang Lake as an Example

Access to real-time environmental sensing data is key to representing real-time environmental changes in the digital twin of lakes. The visualization of environmental sensing data is an important part of establishing a digital twin of lakes. In the past, environmental sensing data display methods were either charts or two-dimensional map-based visualization methods. Breaking through the traditional visualization methods of environmental sensing data and realizing a multi-dimensional and multi-view display of environmental sensing data in a digital twin of lakes is something that this particular paper tries to resolve. This study proposes a visualization framework to integrate, manage, analyze, and visualize the environmental sensing data in the digital twin of lakes. In addition, this study also seeks to realize the coupling expression of geospatial data and long-term monitoring sequence data. Different visualization methods are used to realize the visualization of environmental sensing data in the digital twin of lakes. Using vector and scalar visualization methods to display ambient wireless sensor monitoring data in a digital twin of lakes provides researchers with richer visualization methods and means for deeper analysis. Using video fusion technology to display environmental sensing video surveillance data strengthens the integration of the virtual environment and real space and saves time for position identification using video surveillance. These findings may also help realize the integration and management of real-time environmental sensing data in a digital twin of lakes. The visualization framework uses various visualization methods to express the monitoring data of environmental wireless sensors and increases the means of visualizing environmental sensing data in the world of a lake digital twin. This visualization framework is also a general approach that can be applied to all similar lakes, or other geographical scenarios where environmental sensing devices are deployed. The establishment of a digital twin of Poyang Lake has certain practical significance for improving the digital management level of Poyang Lake and monitoring its ecological changes. Poyang Lake is used as an example to verify the proposed framework and method, which shows that the framework can be applied to the construction of a lake-oriented digital twin.
An Educational Game to Teach Children about Air Quality Using Augmented Reality and Tangible Interaction with Sensors

Air pollution is known to be one of the main causes of injuries to the respiratory system and even premature death. Gases, particles, and biological compounds affect not only the air we breathe outdoors, but also indoors. Children are highly affected by the poor quality of the air they breathe because their organs and immune systems are still in the developmental stages. To contribute to raising children's awareness to these concerns, this article presents the design, implementation, and experimental validation of an serious augmented reality game for children to playfully learn about air quality by interacting with physical sensor nodes. The game presents visual representations of the pollutants measured by the sensor node, rendering tangible the invisible. Causal knowledge is elicited by stimulating the children to expose real-life objects (e.g., candles) to the sensor node. The playful experience is amplified by letting children play in pairs. The game was evaluated using the Wizard of Oz method in a sample of 27 children aged between 7 and 11 years. The results show that the proposed game, in addition to improving children's knowledge about indoor air pollution, is also perceived by them as easy to use and a useful learning tool that they would like to continue using, even in other educational contexts.
Autonomous Underwater Vehicle Path Tracking Based on the Optimal Fuzzy Controller with Multiple Performance Indexes

Autonomous underwater vehicles (AUVs) are increasingly being used in missions involving submarine cable detection, underwater archaeology, pipeline inspection, military reconnaissance, and so on. It is very important to realize AUV path tracking to accomplish these missions. In this paper, a fuzzy controller based on the established kinematic and dynamic models of AUV systems is presented to solve the AUV path-tracking problem. In order to design the fuzzy controller to exhibit good performance, we select the path length, smoothness, and cross-track position error as the multiple optimization performance indexes for the fuzzy controller. We propose the particle swarm optimization (PSO) algorithm to determine the parameters of the membership functions. Different scenarios are presented to test the performance of the proposed algorithm, including the straight line, sine curve, half-moon shape, Archimedean spiral, and practical paths. The results are given to illustrate the effectiveness and feasibility of the fuzzy controller with the optimization of multiple performance indexes.
New quantum algorithm for visual tracking

Visual tracking, which trains a classifier to distinguish the target from the surrounding environment given an initial sample patch containing the target, plays an important role in computer vision. Yu et al. proposed a quantum algorithm for visual tracking (QVT) [Phys. Rev. A 94, 042311 (2016)] with time complexity OϱZϱZ+ϱX2polylogNϵ based on the framework proposed by Henriques et al. [IEEE Trans. Pattern Anal. Mach. Intell. 7, 583 (2015)], where ϱXZ is the condition number of the data matrix XZ, N is the dimension of the original sample patch, and ϵ is the desired accuracy of the output state. To get a further speedup, we propose a new QVT with time complexity OϱZ1+ϱXpolylogNϵ based on the algorithm of Henriques et al. Our algorithm achieves a quadratic speedup on the condition number ϱX(Z) compared to the algorithm of Yu et al. Also, it shows exponential speedups on N over the classical counterpart when ϱX(Z) and ϵ are OpolylogN. Finally, we extend it to the nonlinear two-dimensional multi-channel case.
An evaluation methodology to determine the actual limitations of a TinyML-based solution

Tiny Machine Learning (TinyML) is an expanding research area based on pushing intelligence to the edge and bringing machine learning techniques to very small devices and embedded systems applications. TinyML reduces energy expenditure, uses low bandwidth communications technologies, and adds more privacy to the developed applications. This work, proposes an evaluation methodology to determine the limitations of a TinyML-based solution starting from creating and preparing the required dataset. Then, the training of the selected machine learning algorithms is detailed, together with the consequent evaluation, and how the experiments must be structured. Four metrics were usedto evaluate the performance of the machine learning algorithms in the various tasks: precision, recall, f1-score, and accuracy. Finally, a comparison ofthe performance of a wide range of machine learning algorithms (i.e., Random Forest, Decision Tree, Support Vector Classifier, Logistic Regression, Gaussian Naive Bayes, and Multi-Layer Perceptron) is presented.
Computational tool for technical-economic analysis of photovoltaic microgeneration in Brazil

This paper proposes a new computational tool, called SolarEnergy, which was implemented through an innovative methodology to perform technical-economic analysis of photovoltaic (PV) microgeneration in Brazil. SolarEnergy calculates energy flow and energy bill savings based on the rules of the Brazilian NEM scheme. From the maximization of the net present value, SolarEnergy provides the optimal nominal power according to the orientation and slope of PV panels and the electricity consumption profile. This functionality is crucial to avoid undersizing or oversizing, which commonly occurs in practice. In addition, the tool provides the following information: (i) electricity generation, (ii) net present value, (iii) modified internal rate of return, (iv) discounted payback time, and (v) sensitivity analysis of key parameters (e.g., nominal power, pv system cost, electricity tariff, and minimum attractive rate of return). Currently, such particularities are characterized as relevant differences in relation to other computational tools. The validation and applicability of SolarEnergy are demonstrated from case studies of PV systems installed in the state of Goiás, Brazil.
Replication of the natural selection of bad science

This study reports an independent replication of the findings presented by Smaldino and McElreath (Smaldino, McElreath 2016 R. Soc. Open Sci.3, 160384 (doi:10.1098/rsos.160384)). The replication was successful with one exception. We find that selection acting on scientist's propensity for replication frequency caused a brief period of exuberant replication not observed in the original paper due to a coding error. This difference does not, however, change the authors' original conclusions. We call for more replication studies for simulations as unique contributions to scientific quality assurance.
Sign Language Detection for Deaf and Dumb students using Deep learning: Dore Idioma

Dore Idioma, also known as a hand in Arabic and sign language in Albanian, will be a futuristic website for the deaf-dumb or deaf-mute people as it provides a solution for people who face difficulty speaking and hearing. According to various surveys and stats from different deaf-mute organizations, 63% [21] of the world's population is deaf-mute. To help such people, we devised the idea of Dore Idioma. The fundamental functionality of Dore Idioma is to convert sign language (hand gesture). As communication takes place between ordinary and deaf-mute people, therefore, it will be easy for everyone to communicate through Dore Idioma. It is creating a user-friendly application that helps the user to have an approach to the translator very quickly. We will use image labeling so that input is given via the user; the information is then compared with the pre-trained detection model, after which the detected sign is cross-referenced with the database, and the final output will be generated. Various other models/software has been created. Still, they were not efficient and user-friendly enough and implementing. Dore Idioma might help in practical and better communication amongst ordinary and deaf-mute people. The pre-trained model used to create Dore Idioma is MobileNet version 2 (Convolutional Neural Network). Our current model is working on an accuracy of 99% and a total loss of 0.38%.