Reviewerly/exHarmony huggingface dataset:
A practical method for considering shading on photovoltaics systems energy yield

Along with the widespread adoption of solar energy, it is fundamental to develop methods and tools that help practitioners during the design phase of photovoltaic (PV) systems. Currently, multiple commercial software can quantify a particular location’s annual energy yield while including the horizon’s shading effect (e.g., mountains, buildings, and trees). To do so, precise information about the PV system’s surroundings is necessary. This information is gathered by specialized equipment or by having access to satellite imagery. Therefore, to offer a more practical approach, we propose a method that requires only a cellphone camera, a fixed point for taking a panoramic photograph, and a compass. Once the panoramic image is taken, the obstacles’ width, height, and altitude are calculated, and the skyline is built. With this information, the method correlates the position of the sun with meteorological data to include the effect of shading on direct irradiation. The method was tested using one–year meteorological data to determine the best orientation of a PV system. The image processing method and the general method were validated by getting PV power generation data and aerial images and comparing them to the method’s predictions. Therefore, we introduce a method that, with low computational complexity, facilitates the study of shading on the performance of PV systems.
Area-Ware Adaptive Image Compression

Recently, compression is essential to reduce large traffic generated from video or image-based services such as OTT, IOT, smart city, and self-driving cars. The high data compression reduces data traffic, but causes information loss and data quality deterioration. In this paper, we shall propose a new efficient compression method which increases the compression ratio while maintaining the image quality by partitioning the image into 5 different areas each with the different quantization values according to their priority such as intensity, location, size and correlation. We exploit different compression ratio for each of the areas divided based on saliency map according to their visual priority order in order to fully reflect the visual reaction degree of human visual system. We shall show that our method dramatically increases data preservation and compression efficiency by compressing each area with the different quantization values while using the less memory.
Quantum Artificial Hummingbird Algorithm for Feature Selection of Social IoT

The Internet of Things (IoT) benefits from social networking platforms in establishing and enhancing social-oriented services, information, and autonomous social relationships. Social IoT (SIoT) systems can boost the user experience in the real world in several applications, including healthcare, transportation, and entertainment. However, the collected data from various interconnected SIoT systems is massive, demanding robust and efficient processing algorithms, feature extraction, selection, and inference. This work presents an enhanced Artificial Hummingbird algorithm (AHA) for feature selection (FS). The enhanced version of AHA is performed using the advantages of Quantum-based optimization. The main aim of using Quantum is to improve the population’s exploration ability while discovering feasible regions. Extensive experiments utilizing eighteen UCI datasets were conducted to validate the developed FS method, QAHA. The QAHA is compared with other FS methods, and the experimental established its efficiency. Moreover, a set of four datasets from SIoT are used to evaluate the applicability of QAHA to the real-world setting. The results using these datasets indicate the high performance of QAHA to increase the accuracy by decreasing the number of features. In the case of UCI datasets, the average accuracy of the developed QAHA is 93% among the eighteen datasets. Whereas, In the case of the SIoT datasets, the developed QAHA has an accuracy of nearly 90.7%, 98.7%, 92.2%, and 84.6% for the Trajectory, GAS sensors, Hepatitis, and MovementAAL datasets, respectively.
xOpat: eXplainable Open Pathology Analysis Tool

Abstract Histopathology research quickly evolves thanks to advances in whole slide imaging (WSI) and artificial intelligence (AI). However, existing WSI viewers are tailored either for clinical or research environments, but none suits both. This hinders the adoption of new methods and communication between the researchers and clinicians. The paper presents xOpat, an open‐source, browser‐based WSI viewer that addresses these problems. xOpat supports various data sources, such as tissue images, pathologists' annotations, or additional data produced by AI models. Furthermore, it provides efficient rendering of multiple data layers, their visual representations, and tools for annotating and presenting findings. Thanks to its modular, protocol‐agnostic, and extensible architecture, xOpat can be easily integrated into different environments and thus helps to bridge the gap between research and clinical practice. To demonstrate the utility of xOpat, we present three case studies, one conducted with a developer of AI algorithms for image segmentation and two with a research pathologist.
An Informed Decision Support Framework from a Strategic Perspective in the Health Sector

This paper introduces an informed decision support framework (IDSF) from a strategic perspective in the health sector, focusing on Saudi Arabia. The study addresses the existing challenges and gaps in decision-making processes within Saudi organizations, highlighting the need for proper systems and identifying the loopholes that hinder informed decision making. The research aims to answer two key research questions: (1) how do decision makers ensure the accuracy of their decisions? and (2) what is the proper process to govern and control decision outcomes? To achieve these objectives, the research adopts a qualitative research approach, including an intensive literature review and interviews with decision makers in the Saudi health sector. The proposed IDSF fills the gap in the existing literature by providing a comprehensive and adaptable framework for decision making in Saudi organizations. The framework encompasses structured, semi-structured, and unstructured decisions, ensuring a thorough approach to informed decision making. It emphasizes the importance of integrating non-digital sources of information into the decision-making process, as well as considering factors that impact decision quality and accuracy. The study’s methodology involves data collection through interviews with decision makers, as well as the use of visualization tools to present and evaluate the results. The analysis of the collected data highlights the deficiencies in current decision-making practices and supports the development of the IDSF. The research findings demonstrate that the proposed framework outperforms existing approaches, offering improved accuracy and efficiency in decision making. Overall, this research paper contributes to the state of the art by introducing a novel IDSF specifically designed for the Saudi health sector.
PENTINGNYA PEMBAHARUAN TEKNOLOGI MEDIA INFORMASI TERHADAP KEBUTUHAN PERPUSTAKAAN DAN PUSTAKAWAN

The renewal of information media technology is important to the needs of libraries and librarians because the demands of technological advances are growing.In the era of the Industrial Revolution 4.0, the library is not only a place to store and borrow books, but also a place where the latest information and others are available.In this study, the author uses a literature study by collecting data from literature materials such as scientific journals or falid articles.Efforts made by libraries in developing the need for information technology by making new creations such as utilizing internet services and updating the features of library work aids.And the effort that the librarian can do is to add, expand and update the science of technology in order to support the running of the library.The development of information technology also produces tools that assist humans in recording information and storing files using computers and other storage media.
The Predicament of College English Curriculum Education Reform under the Background of Big Data and Internet of Things

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
Quantum secure direct communication scheme based on the mixture of single photon and Bell state with two way authentication

In response to the demand for identity authentication in quantum secure direct communication, this paper proposes a quantum secure direct communication scheme based on a mixture of single photon and Bell state, by combining the bidirectional identity authentication. Before communication begins, both parties share a series of secret information to prepare a series of single photon and Bell state particles. Encoding four single photons and four Bell states yields eight types of encoded information, followed by identity authentication. The first step in identity authentication is to use a single photon to verify the legitimacy of the receiver. If the error exceeds the given threshold, it indicates the presence of eavesdropping. Otherwise, the channel is safe. Then, Bell state particles are used to verify the legitimacy of the sender, and the threshold is also used to determine whether there is eavesdropping. The present method is the same as previous one. If the error rate is higher than the given threshold, it indicates the existence of third-party eavesdropping. Otherwise, it indicates that the channel is secure. As for the specific verification method, it will be explained in detail in the article. Afterwards, Bell state particles are mixed with a single photon as a transmission carrier, and eavesdropping detection particles are added whenever the quantum state is sent. However, once the eavesdropper intercepts the transmitted particles, owing to incomplete information obtained, the eavesdropper is unable to recover the original information, and the eavesdropping behavior will be immediately detected, thus terminating communication. In this scheme, single photon and Bell states are fully utilized, and hybrid communication can effectively improve transmission efficiency, encoding capability, and quantum bit utilization. Security analysis shows that this scheme can resist common external and internal attacks such as interception/measurement replay attacks, auxiliary particle attacks, and identity impersonation attacks. The analysis of efficiency and encoding capacity shows that the transmission efficiency of this scheme is 1, the encoding capacity is 3 bits per state, and the quantum bit utilization rate is 1. Compared with other schemes, this scheme has significant advantages because it uses different particles for bidirectional authentication, making it more difficult for attackers to crack, and thus it has higher security than traditional schemes.
Multi-Scale Batch-Learning Growing Neural Gas Efficiently for Dynamic Data Distributions

Growing neural gas (GNG) has many applications, including topology preservation, feature extraction, dynamic adaptation, clustering, and dimensionality reduction. These methods have broad applicability in extracting the topological structure of 3D point clouds, enabling unsupervised motion estimation, and depicting objects within a scene. Furthermore, multi-scale batch-learning GNG (MS-BL-GNG) has improved learning convergence. However, it is only implemented on static or stationary datasets, and adapting to dynamic data remains difficult. Similarly, the learning rate cannot be increased if new nodes are added to the existing network after accumulating errors in the sampling data. Next, we propose a new growth approach that, when applied to MS-BL-GNG, significantly increases the learning speed and adaptability of dynamic data distribution input patterns. This method immediately adds data samples as new nodes to existing networks. The probability of adding a new node is determined by the distance between the first, second, and third closest nodes. We applied our method for monitoring a moving object at its pace to demonstrate the usefulness of the proposed model. In addition, optimization methods are used such that processing can be performed in real-time.
Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models

Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggles. We can incorporate efficient strategies such as surrogate-assisted optimisation to address the challenges; however, implementing inter-process communication for surrogate-based model training is difficult. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. We first test the framework on a set of benchmark optimisation problems and then apply it to a geoscientific model that features a landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computational time while retaining optimisation solution accuracy through the use of surrogates in a parallel computing environment. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in a better understanding of paleoclimate and geomorphology.
Cultural Literacy in Teaching English for Guiding

Cultural literacy is highly needed by those who deal with tourism area as the subject of the study. English for Guiding is one of the courses that belongs to Diploma 3 English of Ganesha University of Education and it deals with tourism. This study aims at discussing the need analysis of cultural literacy in teaching English for guiding in order to know about cultural components involved in the teaching process. This study is descriptive study which describe about the need of the students in cultural literacy. The data were gathered through questionnaires to gain the information about the cultural elements in cultural literacy that can support the students in learning English for Guiding and prepare them to become skillful in English proficiency as well as competent and knowledgable in contents. The data were gathered by using questionnaire in order to gain the information about the students’ need on the cultural literacy in lerning English for guiding. The finding shows that there are seven big cultural elements such as 1) symbols; 2) language; 3) norms; 4) rituals; 5) changing norms and belief; 6) values which provide the consideration of what is good and bad, desirable and undesirable; 7) artifacts. The implications of this research are hoped that in the future students will be able to be professional and competent through cultural literacyg.
Morphology-Based Spell Checker for Dawurootsuwa Language

Processing of textual information by using word-processing tools is extremely increased due to the presence of misspelled or erroneous words. In order to minimize these misspelled words from digital information, different spellchecker tools are needed. A plenty of works are performed in technological favored languages like English and European languages but not for an underresourced language like Dawurootsuwa. The primary idea behind a morphology-based spellchecker is to use a dictionary lookup approach with morphological properties of the language to reduce dictionary size while also handling word inflection, derivation, and compounding. Two distinct tests were carried out in this work to evaluate the performance of a morphology-based spellchecker: error detection and error correction. The Hunspell dictionary format was utilized to construct the root words in this study, which included a total of 5,000 root words and more than 2,500 morphological rules along with 3,156 unique words for testing. The experimental result showed the overall spell error detection performance of 90.4% and the overall spell error correction performance of 79.31%. Moreover, we are working further towards developing a real word spelling checker that incorporate more numbers of language rules.
Security of Neural Network-Based Key Agreement Protocol for Smart Grids

Recent developments in quantum computing pose a significant threat to the asymmetric cryptography currently in use. Neural cryptography offers a potential alternative that is resistant to attacks of known quantum computer algorithms. The considered solution is lightweight and computationally efficient. If a quantum computer algorithm were successfully implemented, it could expose IoT sensors and smart grid components to a wide range of attack vectors. Given the lightweight nature of neural cryptography and the potential risks, neural cryptography could have potential applications in both IoT sensors and smart grid systems. This paper evaluates one of the suggested enhancements: the use of integer-valued input vectors that accelerate the synchronization of the Tree Parity Machine. This enhancement introduces a new parameter M that indicates the minimum and maximum values of input vector elements. This study evaluates the nonbinary version of the mutual learning algorithm in a simulated insecure environment. The results indicate that, while the Nonbinary Tree Parity Machine may involve some trade-offs between security and synchronization time, the speed improvement is more substantial than the decrease in security. The impact of this enhancement is particularly significant for smaller adjustments to parameter M.
A new path of visual communication from the perspective of the metaverse —— Take Liu Yexi’s Internet Communication Spectacle as an Example

In 2021, the “Metaverse” concept became a big hit. As an exceptional media concept, the metaverse is closely related to the human sensory system, especially visual communication. Through the method of literature research and case analysis, combined with the recent publicity craze of “Liu Yexi” and from the perspective of the combination of the metaverse and short video, this paper argues that the richness and abstraction of the concept of metaverse require short video as a new form of communication. On this basis, the practical experience of “Liu Yexi” visual communication is analyzed from narrative logic, innovation leap, and visual spectacle. Then it is pointed out that the capitalization trend of artificial intelligence is the potential risk the metaverse may face in the long-term development.
Low-resolution Face Recognition and Sports Training Action Analysis based on Wireless Sensors

In order to improve the effect of sports strategy formulation, this paper combines artificial intelligence assisted agile Internet of Things to conduct research on sports simulation scheme, and builds an intelligent sports simulation system.The sports simulation model designed in this paper can change the mechanism parameters according to the actual situation of the moving object, establish the fixed coordinate system and local coordinate system of the parallel platform model, and establish the mathematical model of the parallel platform.Moreover, according to the principle of vector closure, the inverse kinematics position solution is analyzed, and the inverse kinematic velocity solution is derived by using the equation of the inverse position solution.The experimental research results show that the sports simulation system based on artificial intelligence-assisted agile Internet of Things proposed in this paper has a good performance in sports simulation.
Quantum imaginary-time control for accelerating the ground-state preparation

Quantum computers have been widely speculated to offer significant advantages in obtaining the ground state of difficult Hamiltonian in chemistry and physics. In this work, we first propose a Lyapunov control-inspired strategy to accelerate the well-established imaginary-time method for ground-state preparation. We also dig for the source of acceleration of the imaginary-time process under Lyapunov control with theoretical understanding and dynamic process visualization. To make the method accessible in the noisy intermediate-scale quantum era, we further propose a variational form of the algorithm that could work with shallow quantum circuits. Through numerical experiments on a broad spectrum of realistic models, including molecular systems, 2D Heisenberg models, and Sherrington-Kirkpatrick models, we show that imaginary-time control may substantially accelerate the imaginary-time evolution for all systems and even generate orders of magnitude acceleration (suggesting order-of-magnitude acceleration) for challenging molecular Hamiltonians involving small energy gaps as impressive special cases. Finally, with a proper selection of the control Hamiltonian, the new variational quantum algorithm does not incur additional measurement costs compared to the original variational quantum imaginary-time algorithm.
Data Distribution Impact on Preserving Privacy in Centralized and Decentralized Learning

Since its inception, Federated Learning (FL) has successfully dealt with various challenges, such as facilitating training across multiple clients without compromising raw data, preserving individual data privacy, and establishing secure collaboration environments across different devices. Despite its potential benefits, FL still faces privacy and security obstacles, including data leakage, model poisoning, and compliance risks. To address these privacy concerns, the FL domain has widely employed the technique of differential privacy, which involves introducing noise to client data without significantly altering the statistical properties of query results. However, the use of differential privacy can potentially impact the data’s utility by reducing model accuracy. To overcome this problem, we propose a novel approach called Distribution Invariant Local Differential Privacy for Federated Learning (DILDP-FL). DILDP-FL is based on the distribution-invariant privatization method known as DIP. It transforms and perturbs the data while employing suitable transformations to achieve query results similar to those obtained from the original data. Our experimental findings demonstrate that the proposed DILDP-FL technique, when applied to local clients in the FL context, outperforms the conventional Differentially Private Stochastic Gradient Descent. Additionally, we have observed that DILDP-FL performs at least as well as, if not better than, traditional machine learning techniques.