Reviewerly/exHarmony huggingface dataset:
Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity

Image deraining is a typical low-level image restoration task, which aims at decomposing the rainy image into two distinguishable layers: the clean image layer and the rain layer. Most of the existing learning-based deraining methods are supervisedly trained on synthetic rainy-clean pairs. The domain gap between the synthetic and real rains makes them less generalized to different real rainy scenes. Moreover, the existing methods mainly utilize the property of the two layers independently, while few of them have considered the mutually exclusive relationship between the two layers. In this work, we propose a novel non-local contrastive learning (NLCL) method for unsupervised image deraining. Consequently, we not only utilize the intrinsic self-similarity property within samples but also the mutually exclusive property between the two layers, so as to better differ the rain layer from the clean image. Specifically, the non-local self-similarity image layer patches as the positives are pulled together and similar rain layer patches as the negatives are pushed away. Thus the similar positive/negative samples that are close in the original space benefit us to enrich more discriminative representation. Apart from the self-similarity sampling strategy, we analyze how to choose an appropriate feature encoder in NLCL. Extensive experiments on different real rainy datasets demonstrate that the proposed method obtains state-of-the-art performance in real deraining.
CKGS: A Way Of Compressed Key Guessing Space to Reduce Ghost Peaks

Differential power analysis (DPA) is disturbed by ghost peaks.There is a phenomenon that the mean absolute difference (MAD) value of the wrong key is higher than the correct key.We propose a compressed key guessing space (CKGS) scheme to solve this problem and analyze the AES algorithm.The DPA based on this scheme is named CKGS-DPA.Unlike traditional DPA, the CKGS-DPA uses two power leakage points for a combined attack.The first power leakage point is used to determine the key candidate interval, and the second is used for the final attack.First, we study the law of MAD values distribution when the attack point is AddRoundKey and explain why this point is not suitable for DPA.According to this law, we modify the selection function to change the distribution of MAD values.Then a keyrelated value screening algorithm is proposed to obtain key information.Finally, we construct two key candidate intervals of size 16 and reduce the key guessing space of the SubBytes attack from 256 to 32.Simulation experimental results show that CKGS-DPA reduces the power traces demand by 25% compared with DPA.Experiments performed on the ASCAD dataset show that CKGS-DPA reduces the power traces demand by at least 41% compared with DPA.
The Role of Professional Practical Labor in the Cultivation of Master’S Degree Candidates

There is a common problem of 'neglecting moral education, emphasizing intellectual education and forgetting labor education' in the current postgraduate training program of colleges and universities.It seriously affects the training quality of high-level applied talents.In combination with the resources of the forestry major in Beijing University of Agriculture and the postgraduate training practice of the breeding research group, this paper summarizes and discusses the role of professional practical labor to postgraduates in improving professional cognition, professional education, and cultivation quality, determining research topics, and promoting their all-round development.The research results are to provide a basis for exploring the training ways and revising the training program of postgraduates majoring in forestry.
An Analysis of Variations in the Effectiveness of Query Performance Prediction

A query performance predictor estimates the retrieval effectiveness of a system for a given query. Query performance prediction (QPP) algorithms are themselves evaluated by measuring the correlation between the predicted effectiveness and the actual effectiveness of a system for a set of queries. This generally accepted framework for judging the usefulness of a QPP method includes a number of sources of variability. For example, "actual effectiveness" can be measured using different metrics, for different rank cut-offs. The objective of this study is to identify some of these sources, and investigate how variations in the framework can affect the outcomes of QPP experiments. We consider this issue not only in terms of the absolute values of the evaluation metrics being reported (e.g., Pearson's r, Kendall's $$\tau $$ ), but also with respect to the changes in the ranks of different QPP systems when ordered by the QPP metric scores. Our experiments reveal that the observed QPP outcomes can vary considerably, both in terms of the absolute evaluation metric values and also in terms of the relative system ranks. We report the combinations of QPP evaluation metric and experimental settings that are likely to lead to smaller variations in the observed results.
Image Quality Assessment Based on Sparse Reference Information

The last few years have seen the appearance of a new sparse reference-based free energy principle, which demonstrates that an input visual signal was always strived to be comprehend by the human visual system (HVS) through removing the undetermined portions. By this inspiration, we in this paper put forward an image quality assessment (IQA) model based on sparse reference information. In essential, as compared with the classical good-performance FEDM model that was developed with a linear local autoregressive (AR) model, the proposed sparse reference (SR) IQA model is a simplified version of FEDM, achieving comparable performance but hundreds of times faster implementation. More specifically, this paper introduces the extremely sparse random sampling method into the FEDM model. Experimental results on the most well-known LIVE IQA database illustrate the effectiveness along with efficiency of our SR-IQA model as compared with the typical full-reference IQA models and the cutting-edge SR IQA models.
The Self-Learning Gate for Quantum Computing

Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual.However, self-learning loses importance if it does not follow the scientific methodology for building and organizing information.The case becomes harder if the science is new and few scientific sources are available.Quantum computing is one of the new sciences in computer science and needs the support of specialists to develop it.Quantum computing overlaps with many sciences such as physics, chemistry, and mathematics, so any student in one of the previous disciplines may lose the correct self-learning path to find themselves learning the details of another discipline that does not achieve their goals.This article motivates students and those interested in computer science to begin studying the science of quantum computing and choose the same specialization that suits their interests.The article also provides a roadmap for self-learning steps to protect the learner from losing the correct learning path.I have categorized the stages of learning quantum computing into four steps through which all the essential basics can be learned, provided the goals mentioned in each stage which should be achieved.The learning strategy proposed in this article corresponds with individuals' self-learning rules.Through my personal experience, the proposed learning strategy has proven its effectiveness in building information in an enjoyable scientific way.
AdaConfigure: Reinforcement Learning-Based Adaptive Configuration for Video Analytics Services

The configuration in video analytics defines parameters including frame rate, image resolution, and model selection for video analytics pipeline, and thus determines the inference accuracy and resource consumption. Traditional solutions to select a configuration are either fixed (i.e., the same configuration is used all the time) or periodically adjusted using a brute-force search scheme (i.e., periodically trying different configurations and selecting the one with the best performance), and thus suffer either low inference accuracy or high computation cost to find a proper configuration timely. To this end, we propose a video analytical configuration adaptation framework called AdaConfigure that dynamically selects video configuration without resource-consuming exploration. First, we design a reinforcement learning-based framework in which an agent adaptively chooses the configuration according to the spatial and temporal features of the current video stream. In particular, we use a video segmentation strategy to capture the characteristics of the video stream with much-reduced computation cost: profiling uses only 0.2–2% computation resources as compared to a full video. Second, we design a reward function that considers both the inference accuracy and computation resource consumption so that the configuration achieves good accuracy and resource consumption trade-off. Our evaluation experiments on an object detection task show that our approach outperforms the baseline: it achieves 10–35% higher accuracy with a similar amount of computation resources or achieves similar accuracy with only 10–50% of the computation resources.
Relevance of Blockchain in Revolutionizing Health Records

Healthcare breaches and fragmented data in the healthcare sector have been a pressing issue for a long time. Though standards of data systems and information security have been increasing with time, there has not been any established approach to keep health records secure and easy to use. Blockchain technology offers a solution—and healthcare organizations are increasingly taking notice of its transformative potential. It has of late been one of the most promising fields of study in the research sector. Though it is still in its early development stages, improvements are being devised every day to make it better than before. In this study, we aim to discuss the innovation of blockchain technology and the way it may solve the current issues in the healthcare sector. A framework is presented that could be used to implement blockchain technology in the healthcare industry for Electronic Health Records (EHR), evaluating the various perspectives around health data, including data protection, security, control, and storage. Blockchain, being one of the most encouraging technologies lately has imparted immense benefits to the health care sector due to security, privacy, confidentiality, and decentralization. It has the potential to comprehensively manage patient records.
Percepciones de los estudiantes del centro de idiomas sobre el aprendizaje de inglés virtual como competencia

English is one of the most widely used languages worldwide, which is why there is a need to introduce this language in the educational field, especially in higher education, since today it has become a basic need for professionals. This study was focused on analyzing students' perceptions of online English learning as proficiency at the Languages Center of Technical University of Cotopaxi. A qualitative approach and a case study method was used. As a data collection technique, an unstructured interview was applied to ten students from different careers and English levels. The results revealed that the majority of students choose English as a proficiency because it is one of the most in-demand languages for getting a job. Moreover, some students take learning English only as a mandatory requirement before graduation. Additionally, inside online English learning there are certain complications with respect to the development of speaking skills. Furthermore, low student participation is due to lack of interest or fear of making mistakes. Therefore, it is necessary to implement different techniques and methods to carry out the online teaching-learning process of English as a proficiency in an effective way and thus be able to achieve all the expectations that students have.
addHessian: Combining quasi-Newton method with first-order method for neural network training

First-order methods such as SGD and Adam are popularly used in training Neural networks. On the other hand, second-order methods have shown to have better performance and faster convergence despite their high computational cost by incorporating the curvature information. While second-order methods determine the step size by line search approaches, first-order methods achieve efficient learning by devising a way to adjust the step size. In this paper, we propose a new learning algorithm for training neural networks by combining first-order and second-order methods. We investigate the effectiveness of our proposed method when combined with popular first-order methods - SGD, Adagrad, and Adam, through experiments using image classification problems.
Correction to: Improving Amphetamine-Type Stimulants Drug Classification Using Binary Whale Optimization Algorithm as Relevant Descriptors Selection Technique

In the original version of the book, the misspelt co-author name “Satrya Fajr Pratama” has been changed to “Satrya Fajri Pratama” in Chapter “Improving Amphetamine-Type Stimulants Drug Classification Using Binary Whale Optimization Algorithm as Relevant Descriptors Selection Technique”. The erratum chapter and the book have been updated with the change.
Skills demand, digital skills and skill formation in the European Union in the digital era

Developed economies have recently become more complex than ever before. As the European Union is transforming into a knowledge-based economy, more and more attention is paid to skill formation, in view of the fact that skills have become the most valuable resource in the twenty-first century, often available only scarcely for economic actors. All this implies that, in order to maintain and/or develop competitiveness of the European Union - as well as of the member states -, the knowledge capital base of an economy has to be developed and continuously maintained. Digital skills started to play an important role in the process and the COVID-19 pandemic has accelerated digital skills demand in many occupations, especially non-ICT ones. The aim of this paper is to examine the needs and the importance of digital skills in occupations across the EU through a literature review and descriptive statistics, and to outline possible solutions to develop digital skills use in this forming new ecosystem of economies, digital technologies and the humans operating and applying them.
Application of Multicategory Handwritten Letters, Numbers and Symbols Recognition Based on Convolutional Neural Networks

Handwritten Character Recognition (HCR) is convenient for people to save handwriting information in digital form in a short time. However, most current works can only recognize a single type of character, which is not enough for practical use. Moreover, the input is relatively limited since most of the works do not support Red, Green, and Blue (RGB) input or high-resolution input. Furthermore, the published code-based recognition process is unfriendly to non-programmer users. In this work, an improved recognition system of handwritten characters, integers, and symbols is constructed by applying a deep learning algorithm called convolutional neural networks (CNN). The proposed CNN model employs the structure of the MobileNet. To design this model, Tensorflow is adopted to achieve the stated goal. At the same time, a rather huge training dataset is applied to ensure a relatively decent accuracy. Lastly, we compile our model with an optimizer that implements the Adam algorithm and set the epochs to 10 when we fit the model. The experimental results indicate that the proposed method achieved 0.88 for accuracy on the testing data, which is satisfactory in a real application. Besides a Graphical User Interface (GUI) has been formed so that even non-programmer users can easily use it with their electronic devices.
Desenvolvimento de Aplicações Paralelas Adaptativas: Uma Visão de Duas Décadas (2001-2021)

The book introduced in XXII Escola Regional de Alto Desempenho da Região Sul (ERAD/RS) – event coordinated by Sociedade Brasileira de Computação (SBC) and Comissão Regional de Alto Desempenho do Estado do Rio Grande do Sul (CRAD/RS) with the aim of qualifying professionals from the south region of Brazil – , involves the integration of parallel programing researchers. Written in five chapters, this book is able to provide a grand variety of convergent content, focusing on performance. &nbsp; In its first chapter, entitled “Desenvolvimento de Aplicações Paralelas Adaptativas: Uma Visão de Duas Décadas (2001-2021)”, the authors explain about the evolution of the parallel &nbsp;architectures and the transcription of their uses in dynamic environment. The theme provoques an interesting reflection not only for already-consolidated academics, but also for young students searching for new challenges. In the following two chapters, the main topics are programing languages and technical in parallel computing, named as “Computação de Alto Desempenho em Julia", which refers to modern language, easily used and learn; and "Introdução à Programação com Memória Persistente", which discuss about the motivation and problems related to consistency of processors’ datas with persistent memory (PM) In the fourth chapter, the essential aspects, the evaluation and the differences from the programs aiming to achieve the Gaming section were summarized and discussed. Lastly, the outcome of the book highlights the omnipresent theme from research turned to the processing of high performance (PAD), “Apresentação de Resultados Experimentais para Processamento de Alto Desempenho em R”, which proposes ways to better dispose and reproduc the datas in academic texts and experiments.
Co-R-Calculus for PL

R-calculus (a belief revision operator) preserves satisfiability [1, 3]. A theory $$\mathbf{X}$$ is $$*$$ -satisfiable if there is an assignment v such that $$v(X)=*$$ for each $$X\in \mathbf{X}.$$ Given a theory $$\mathbf{X}$$ and a formula X, if $$\mathbf{X}$$ is $$*$$ -satisfiable with X then to revise X by $$\mathbf{X}$$ we obtain $$\mathbf{X}\cup \{X\},$$ denoted by $$\Delta |X\Rightarrow \Delta ,X;$$ otherwise, we obtain $$\mathbf{X},$$ denoted by $$\mathbf{X}|X\Rightarrow \mathbf{X}.$$ In another form, we have the following two rules: $$(+)\ \dfrac{\vdash ^*_{\texttt {e}}\mathbf{X},X}{\mathbf{X}|X\Rightarrow \mathbf{X},X}\qquad \qquad \quad \,\,\,\, (-)\ \dfrac{\vdash _*^{\texttt {a}}\mathbf{X},X}{\mathbf{X}|X\Rightarrow \mathbf{X}}.$$ It is clear that $$\mathbf{X}\not \vdash \lnot X$$ is nonmonotonic in $$\mathbf{X},$$ i.e., for any theory $$\mathbf{X}'\supseteq \mathbf{X},$$ $$\mathbf{X}|X\Rightarrow \mathbf{X},X$$ does not imply $$\mathbf{X}'|X\Rightarrow \mathbf{X},X';$$ and $$\mathbf{X}\vdash \lnot X$$ is monotonic in $$\mathbf{X},$$ i.e., $$\mathbf{X}|X\Rightarrow \mathbf{X}$$ implies $$\mathbf{X}'|X\Rightarrow \mathbf{X}.$$ Therefore, R-calculus consists of a nonmonotonic part and a monotonic part, where the monotonic part corresponds to monotonic tableau proof system $$\mathbf{T}^{\texttt {f}}$$ and the nonmonotonic part corresponds to nonmonotonic tableau proof system $$\mathbf{S}^{\texttt {t}}.$$ .
CGC-NET: Aircraft Detection in Remote Sensing Images Based on Lightweight Convolutional Neural Network

In the past few years, aircraft detection in remote sensing (RS) images has been an important research hotspot, and it is very crucial in plenty of military applications. Based on the high computational cost of the model and numerous parameters, deep convolution neural networks-based algorithms have excellent performance in the aircraft detection task. However, it is still difficult to detect aircraft due to the complex background of RS images, various types of aircraft, and so on. In addition, it is difficult and costly to make labels for satellite-based optical RS images. Consequently, we propose an end-to-end lightweight aircraft detection framework called CGC-NET (a network based on circle grayscale characteristics), which can accurately detect aircraft with a few training samples. There are only a small number of trainable parameters in CGC-NET, which greatly reduces the need for large datasets. Extensive evaluations indicate the excellent performance of CGC-NET, in which the <i>F</i>-score can reach 91.06&#x0025; and the model size is only 0.88 M. Therefore, CGC-NET can be used to accurately detect aircraft targets simply and effectively.
Relationship between the number of elements in constraint satisfaction problems and the computation time of HNN

In this paper, we aim to clarify the computational complexity of artificial neural networks. We investigate the computational complexity of hysteresis neural networks (HNN) for solving constraint satisfaction problems. We confirm that the amount of computation is proportional to the logarithm of the size of the problem.
Analysis and Research of Threat, Attacker and Security Models of Data Depersonalization in Decentralized Networks

Blockchain technology is one of the most attractive models of a decentralized environment that can be used to store, process and forward information, while ensuring the maximum level of its depersonalization. All models of the offender and all attacks aimed at cryptographic transformations remain threats to any blockchain. Since hash functions and digital signature have been used in cryptology for more than a decade, many of these threat models are well studied and researched, with the necessary and strictly substantiated necessary and sufficient conditions for the success of various attacks, developed methods for assessing resilience and building pre-stable cryptographic mechanisms. In this section, we focused on relatively new attacks that occurred only after the appearance of the blockchain and how to protect against them. These attacks are aimed specifically at the way blockchain operates and are not attacks on the cryptographic mechanisms that serve it. Some of the blockchain attacks are common, meaning their main idea can be applied to any blockchain, regardless of the consensus protocol it uses. Other attacks are special attacks, meaning they depend heavily on the type of consensus protocol. We looked at two main models of common blockchain attacks because, unlike attacks on different cryptographic primitives, they are much smaller. In addition to reviewing and comparatively analyzing these attacks, the main ways to counter these attacks are also given, which significantly use the peculiarities of the functioning and maintenance of the blockchain.
A large-scale task scheduling algorithm based on clustering and duplication

Aim: Our research aims to explore a fast and efficient scheduling algorithm. The purpose is to schedule large-scale tasks on a limited number of processors reasonably while improving resource utilization. Methods: This paper proposes a clustering and duplication-based method for large-scale task scheduling on a limited amount of processors. We cluster large-scale task to reduce the scale of the task in our method at first. Second, duplication-based task scheduling is carried out. Third, we optimize the local effect more precisely by deduplication in the last stage. Results: We compare our algorithm with the state-of-the-art algorithms in the article. The results demonstrate that our scheduling scheme obtains about 30% optimization compared to existing large-scale scheduling methods and runs roughly ten times faster than existing duplication-based algorithms when scheduling large-scale tasks to a limited number of processors as compared to similar algorithms. Conclusion: In this paper, we propose a task scheduling algorithm that can decrease the scheduling time of large- scale tasks on a limited number of processors and speed up the global execution time of the task. Further, we will study large-scale task scheduling on heterogeneous processor clusters. Keywords