Reviewerly/exHarmony huggingface dataset:
Short-Term Solar Irradiance Prediction Based on Adaptive Extreme Learning Machine and Weather Data

Concerns over fossil fuels and depletable energy sources have motivated renewable energy sources utilization, such as solar photovoltaic (PV) power. Utilities have started penetrating the existing primary grid with renewable energy sources. However, penetrating the grid with photovoltaic energy sources degrades the stability of the whole system because photovoltaic power depends on solar irradiance, which is highly intermittent. This paper proposes a prediction method for non-stationary solar irradiance. The proposed method uses an adaptive extreme learning machine. The extreme learning machine method uses approximated sigmoid and hyper-tangent functions to ensure faster computational time and more straightforward microcontroller implementation. The proposed method is analyzed using the hourly weather data from a specific site at Najran University. The data are preprocessed, trained, tested, and validated. Several evaluation metrics, such as the root mean square error, mean square error, and mean absolute error, are used to evaluate and compare the proposed method with other recently introduced approaches. The results show that the proposed method can be used to predict solar irradiance with high accuracy, as the mean square error is 0.1727. The proposed approach is implemented using a solar irradiance sensor made of a PV cell, a temperature sensor, and a low-cost microcontroller.
A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model

The volatility of solar energy, geographic location, and weather factors continues to affect the stability of photovoltaic power generation, reliable and accurate photovoltaic power prediction methods not only effectively reduce the operating cost of the photovoltaic system but also provide reliable data support for the energy scheduling of the light storage microgrid, improve the stability of the photovoltaic system, and provide important help for the optimization operation of the photovoltaic system. Therefore, it is an important study to find reliable photovoltaic power prediction methods. In recent years, researchers have improved the accuracy of photovoltaic power generation forecasting by using deep learning models. Compared with the traditional neural network, the Transformer model can better learn the relationship between weather features and has good stability and applicability. Therefore, in this paper, the transformer model is used for predicting ultra-short-term photovoltaic power generation, and the photovoltaic power generation data and weather data in Hebei are selected. In the experiment, the prediction result of the transformer model was compared to the GRU and DNN models to show that the transformer model has better predictive ability and stability. Experimental results demonstrated that the proposed Transformer model outperforms the GRU model and DNN model by a difference of about 0.04 kW and 0.047 kW in the MSE value, and 22.0% and 29.1% of the MAPE error. In addition, the public DC competition dataset is selected for control experiments to demonstrate the general applicability of the transformer model for PV power prediction in different regions.
Fundamental limits on concentrating and preserving tensorized quantum resources

Quantum technology offers great advantages in many applications by exploiting quantum resources like nonclassicality, coherence, and entanglement. In practice, an environmental noise unavoidably affects a quantum system and it is thus an important issue to protect quantum resources from noise. In this work, we investigate the manipulation of quantum resources possessing the so-called tensorization property and identify the fundamental limitations on concentrating and preserving those quantum resources. We show that if a resource measure satisfies the tensorization property as well as the monotonicity, it is impossible to concentrate multiple noisy copies into a single better resource by free operations. Furthermore, we show that quantum resources cannot be better protected from channel noises by employing correlated input states on joint channels if the channel output resource exhibits the tensorization property. We address several practical resource measures where our theorems apply and manifest their physical meanings in quantum resource manipulation.
An aphid inspired metaheuristic optimization algorithm and its application to engineering

The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization Algorithm (AOA), is proposed in this paper. This algorithm simulates the foraging process of aphids with wings, including the generation of winged aphids, flight mood, and attack mood. Concurrently, the corresponding optimization models are presented according to the above phases. At the phase of the flight mood, according to the comprehensive influence of energy and the airflow, the individuals adaptively choose the flight mode to migrate; at the phase of attack mood, individuals use their sense of smell and vision to locate food sources for movement. Experiments on benchmark test functions and two classical engineering design problems, indicate that the desired AOA is more efficient than other metaheuristic algorithms.
Opportunities and Challenges in Fault-Tolerant Quantum Computation

I will give an overview of what I see as some of the most important future directions in the theory of fault-tolerant quantum computation. In particular, I will give a brief summary of the major problems that need to be solved in fault tolerance based on low-density parity check codes and in hardware-specific fault tolerance. I will then conclude with a discussion of a possible new paradigm for designing fault-tolerant protocols based on a space-time picture of quantum circuits.
Are Neural Topic Models Broken?

Recently, the relationship between automated and human evaluation of topic models has been called into question. Method developers have staked the efficacy of new topic model variants on automated measures, and their failure to approximate human preferences places these models on uncertain ground. Moreover, existing evaluation paradigms are often divorced from real-world use. Motivated by content analysis as a dominant real-world use case for topic modeling, we analyze two related aspects of topic models that affect their effectiveness and trustworthiness in practice for that purpose: the stability of their estimates and the extent to which the model's discovered categories align with human-determined categories in the data. We find that neural topic models fare worse in both respects compared to an established classical method. We take a step toward addressing both issues in tandem by demonstrating that a straightforward ensembling method can reliably outperform the members of the ensemble.
An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution

The image up scaling and super resolution gain a attention now a days. Create a high-resolution image with help of a low-resolution image uses in many of the application. The optimization based super-resolution methods is principally driven by the choice of the objective function. Recently artificial intelligence based machine and deep learning methods are also using for the image processing application. It makes easy and efficient way of image super resolution. Recent work has generally centered around limiting the mean squared reproduction blunder. The subsequent evaluations have high pinnacle signal-tocommotion proportions, however they are much of the time lacking high-recurrence subtleties and are perceptually subpar as in they neglect to match the constancy expected at the higher resolution. This paper presents the comparative analysis of the efficient techniques for image super-resolution. Keywords—Image, Super, Resolution, AI, Machine learning, Deep Learning, Perfromance.
Directivity of Quantum Walk via Its Random Walk Replica

Quantum walks (QWs) exhibit different properties compared with classical random walks (RWs), most notably by linear spreading and localization. In the meantime, random walks that replicate quantum walks, which we refer to as quantum-walk-replicating random walks (QWRWs), have been studied in the literature where the eventual properties of QWRW coincide with those of QWs. However, we consider that the unique attributes of QWRWs have not been fully utilized in the former studies to obtain deeper or new insights into QWs. In this paper, we highlight the directivity of one-dimensional discrete quantum walks via QWRWs. By exploiting the fact that QWRW allows trajectories of individual walkers to be considered, we first discuss the determination of future directions of QWRWs, through which the effect of linear spreading and localization is manifested in another way. Furthermore, the transition probabilities of QWRWs can also be visualized and show a highly complex shape, representing QWs in a novel way. Moreover, we discuss the first return time to the origin between RWs and QWs, which is made possible via the notion of QWRWs. We observe that the first return time statistics of QWs are quite different from RWs, caused by both the linear spreading and localization properties of QWs.
Black Box Power: Zones of Uncertainty in Algorithmic Management

This chapter examines algorithmic management and investigates its mechanisms. It is argued that the existing discussion is partly characterised by technological determinism, which firstly assumes comprehensive control and secondly narrows the view to technological aspects. Drawing on the organisation theory of Michel Crozier and Erhard Friedberg, the limited agency of heteronomous actors is analysed. The focus is on the opacity of algorithmic structures, which contributes decisively to the effectiveness of algorithmic management. This mechanism is identified as black box power, leading to an algorithmic self which monitors its actions carefully and is obedient in an anticipatory manner.
Ontology-Driven Visual Analytics Platform for Semantic Data Mining and Fuzzy Classification

Visualization is claimed as one of the essential “V’s” of Big Data since it allows presenting the data in a human-friendly way and is, therefore, a stepping-stone for the Big Data mining process. Visual analytics, in turn, ensures knowledge discovery out of the data through cognitive graphics and filtering capabilities. But to be efficient, visualization and analytics tools have to consider other Big Data “V’s” by handling the large data volumes, keeping up with the data growth and changing velocity, and adapting to the variety of the data representation formats. We propose using ontology engineering methods to create a visual analytics platform controlled by an ontological knowledge base that describes supported data types, input formats, data filters, visual objects, and visualization algorithms, as well as available communication protocols and computing nodes, the platform modules can run on. This allows introducing new functions and distributed computation scenarios to the platform on the fly just by extending the underlying domain ontologies without changing the source code of the platform’s core. The analytics flow inside this platform is described by task ontologies enabling semantic data mining process. As a result, seamless integration with different data sources is achieved, including plain files, databases, and even third-party soft- and hardware solvers. We demonstrate the viability of the approach proposed by solving several data mining and fuzzy classification problems, including the assessment of the citizens’ regional identity according to the mental maps they draw and the reconstruction of ontogenesis of extinct synapsid ‘Titanophoneus potens’ Efremov, 1938.
Analyse On Avalanche Effect In Cryptography Algorithm

Developments in computers and technologies have created an intense need for secure and trustworthy cryptography systems.Highly secure schemes are always desirable for real-world applications.Cryptography requires a secure technique to ensure that the enemy is prevented while securing legitimate users who have access to the data.The avalanche effect is one of the most preferred approaches for determining an algorithm's security in cryptography.In this paper, the experimental process is conducted to examine the sensitivity of the algorithm when a one-bit input is changed in the key, which causes changes on approximately half of the ciphertext.The experiment is performed based on the key avalanche in the proposed block cipher algorithm.These processes were done 32 times based on the 32-bit plaintext on the algorithm and the avalanche effect was calculated in each time based on the ciphertext generated.However, if a block cipher does not exhibit the avalanche effect to a significant degree, it indicates that it has weak randomisation.Hence, the input can be predicted by a cryptanalyst, while only being given the output.From the experiment, the findings revealed that the proposed block cipher algorithm met the key avalanche with a 50% output bit changed in the ciphertext.Therefore, the proposed block cipher scheme satisfies the avalanche effect property, resulting in improved diffusion.
Precise thinking tools and the impact of computer science on professional practice

Introduction. The main intellectual competence of IT professionals includes culture of thinking, the ability to generalize, analyze, and perceive information. But what do we mean by the culture of thinking today? If it is based on consistency and creativity, as leading IT-companies require from university graduates, it immediately indicates the complexity and even inconsistency of the requirements for the expected qualities of thinking from newly-qualified professionals. Because, on the one hand, they need to be able to systematize the available data into a certain structure; on the other hand, they have to abandon the existing one and achieve a fundamentally different solution. The purpose of this article is to identify the core of thinking tools in professional practice and describe precisely them using set-theoretical analysis. Materials and Methods. We proceed from the fact that thinking, as a part of the nature of reality, reaches the level required for professional practice when it satisfies at least three conditions: stability, accuracy and completeness of the description of a particular system or process. Since we cannot extract thinking from historical and cultural way of cognition, which is sometimes called an episteme, it is necessary, first of all, to rely on examples of such a context as the evolution of the linguistic state of the human population and the evolution of the architecture of the design of distributed systems in the information space. The mathematical basis is taken from the experience of teaching and using the set-theoretical method in mathematics disciplines within computer fields of study. Results. Within the framework of the proposed methodology, some important tools of professional thinking are formulated in the precise language of computer science. The structures of stable retention of objective thinking, the accuracy of retention of meaning, the minimum completeness of the system, bricolage and the option of the basic figure of creativity visualization are described. This methodology allows to see the logic of branching in the evolution of different languages, as well as the logic of the evolution of software systems. Conclusions. The set-theoretical tools of thinking reveal the logic within the development of distributed systems in the modern information environment and, apparently, significantly affect the tools of thinking in various fields of knowledge.
Geostandards and Geoanalytical Research Bibliographic Review 2021

This GGR Bibliographic Review is a survey of approximately 5200 geoanalytical publications for the year 2021. Selected articles, numbering over 340, containing measurement results for relevant geological and environmental reference materials are listed with individual summaries of target analytes, relevant reference materials and producers. A brief summary of a selection of these publications is included that highlights notable developments in geoanalytical studies, newly developed or characterised RMs, and new datasets of established reference materials that have been re‐analysed using improved or state‐of‐the‐art measurement techniques.
Fast Labeled Spanning Tree in Binary Irregular Graph Pyramids

Irregular Pyramids are powerful hierarchical structures in pattern recognition and image processing.They have high potential of parallel processing that makes them useful in processing of a huge amount of digital data generated every day.This paper presents a fast method for constructing an irregular pyramid over a binary image where the size of the images is more than 2000 in each of 2/3 dimensions.Selecting the contraction kernels (CKs) as the main task in constructing the pyramid is investigated.It is shown that the proposed fast labeled spanning tree (FLST) computes the equivalent contraction kernels (ECKs) in only two steps.To this purpose, first, edges of the corresponding neighborhood graph of the binary input image are classified.Second, by using a total order an efficient function is defined to select the CKs.By defining the redundant edges, further edge classification is performed to partition all the edges in each level of the pyramid.Finally, two important applications are presented : connected component labeling (CCL) and distance transform (DT) with lower parallel complexity ( ()) where the is the diameter of the largest connected component in the image.
The Development and Future of the <i>Cataloguing Code of Ethics</i>

The Cataloguing Code of Ethics, released in January 2021, was the product of a multi-national, multi-year endeavor by the Cataloging Ethics Steering Committee to create a useful framework for the discussion of cataloging ethics. The six Cataloging Ethics Steering Committee members, based in the United States, United Kingdom, and Canada, recount the efforts of the group and the cataloging community leading up to the release of the Code, as well as provide their thoughts on the challenges of creating the document, lessons learned, and the future of the Code.
Zero-Shot Text Classification with Self-Training

Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference datasets have been widely adopted as zero-shot classifiers due to their promising results and off-the-shelf availability. However, the fact that such models are unfamiliar with the target task can lead to instability and performance issues. We propose a plug-and-play method to bridge this gap using a simple self-training approach, requiring only the class names along with an unlabeled dataset, and without the need for domain expertise or trial and error. We show that fine-tuning the zero-shot classifier on its most confident predictions leads to significant performance gains across a wide range of text classification tasks, presumably since self-training adapts the zero-shot model to the task at hand.
Multimodal Sentiment Sensing and Emotion Recognition Based on Cognitive Computing Using Hidden Markov Model with Extreme Learning Machine

In today's competitive business environment, exponential increase of multimodal content results in a massive amount of shapeless data. Big data that is unstructured has no specific format or organisation and can take any form, including text, audio, photos, and video. Many assumptions and algorithms are generally required to recognize different emotions as per literature survey, and the main focus for emotion recognition is based on single modality, such as voice, facial expression and bio signals. This paper proposed the novel technique in multimodal sentiment sensing with emotion recognition using artificial intelligence technique. Here the audio and visual data has been collected based on social media review and classified using hidden Markov model based extreme learning machine (HMM_ExLM). The features are trained using this method. Simultaneously, these speech emotional traits are suitably maximised. The strategy of splitting areas is employed in the research for expression photographs and various weights are provided to each area to extract information. Speech as well as facial expression data are then merged using decision level fusion and speech properties of each expression in region of face are utilized to categorize. Findings of experiments show that combining features of speech and expression boosts effect greatly when compared to using either speech or expression alone. In terms of accuracy, recall, precision, and optimization level, a parametric comparison was made.
DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data

Federated learning is a type of privacy-preserving, collaborative machine learning. Instead of sharing raw data, the federated learning process cooperatively exchanges the model parameters and aggregates them in a decentralized manner through multiple users. In this study, we designed and implemented a hierarchical blockchain system using a public blockchain for a federated learning process without a trusted curator. This prevents model-poisoning attacks and provides secure updates of a global model. We conducted a comprehensive empirical study to characterize the performance of federated learning in our testbed and identify potential performance bottlenecks, thereby gaining a better understanding of the system.
FARANE-Q: Fast Parallel and Pipeline Q-Learning Accelerator for Configurable Reinforcement Learning SoC

&lt;p&gt;This paper proposes a FAst paRAllel and pipeliNE Q-learning accelerator (FARANE-Q) for a configurable Reinforcement Learning (RL) algorithm that is implemented in a System on Chip (SoC). In order to overcome the challenges of a dynamic environment and increasing complexity, the proposed work offers flexibility, configurability, and scalability while maintaining computation speed and accuracy. The proposed method includes a HW/SW design methodology for the SoC architecture to achieve flexibility. Moreover, we also propose joint optimizations on algorithm, architecture and implementation in order to obtain optimum (high efficiency) performance, specifically in energy and area efficiency. Furthermore, we implemented the proposed design in a real-time Zynq Ultra96-V2 FPGA platform to evaluate the functionality with real use case of the smart navigation. Experimental results confirm that the proposed accelerator FARANE-Q outperforms state of the art works by achieving throughput up to 148.55 MSps. It corresponds to the energy efficiency of 1632.42 MSps/W per agent for 32-bit and 2465.42 MSps/W per agent for 16-bit FARANE-Q. Moreover, the proposed 16-bit FARANE-Q outperforms others in energy efficiency up to more than 2000×. The designed system also maintains the error accuracy less than 0.4% with optimized bit precision for more than 8 fraction bits. The proposed FARANE-Q also offers a speed up of processing time up to 1795× compared to embedded SW computation executed on ARM Zynq processor and 280× of computation of full software executed on i7 processor. Hence, the proposed work has the potential to be used for smart navigation, robotic control, and predictive maintenance.&lt;/p&gt;