Reviewerly/exHarmony huggingface dataset:
Intelligent Retrieval Method of Approximate Painting in Digital Art Field

With the rapid development of Internet technology and the wide application of image acquisition equipment, the number of digital artwork images is exploding. The retrieval of near-similar artwork images has a wide application prospect for copyright infringement, trademark registration, and other scenes. However, compared with traditional images, these artwork images have the characteristics of high similarity and complexity, which lead to the retrieval accuracy not meeting the demand. To solve the above problems, an intelligent retrieval method of artwork image based on wavelet transform and dual propagation neural network (WTCPN) is proposed. Firstly, the original artwork image is replaced by the low-frequency subimage after wavelet transform, which not only removes redundant information and reduces the dimension of data but also suppresses random noise. Secondly, in order to make the network assign different competition winning units to different types of modes, the dual propagation neural network is improved by setting the maximum number of times of winning neurons. Experimental results show that the proposed method can improve the accuracy of image retrieval, and the recognition accuracy of verification set can reach over 91%.
Agricultural Export, Growth and the Poor in Africa: A Meta Analysis

Over the past decade, a growing number of studies have examined the role of agricultural export in economic growth in Africa. The literature, however, provides conflicting results about the agricultural export-led growth hypothesis. In this study, we aim to examine the impact of agricultural export on economic growth by performing a meta-analysis. Our meta-analysis finds significant presence of negative publication bias in the literature. Using mixed-effect multilevel meta-regression, we find that after correction for publication bias, the average agricultural export elasticity to economic growth is 0.763 for the poor in Africa. Interestingly, agricultural export is growth for the rich in Africa, although the elasticity of GDP is 0.043. These results are consistent with the agricultural export-led growth hypothesis. The implication is that export promotion should be targeted at agricultural output in low-income and lower middle-income countries whereas upper middle-income countries in Africa may focus on non-agricultural export.
A commemoration of the “digital” side of Juan Rosai: a junior’s perspective of the legacy of an all-round pathologist

Juan Rosai, the "Maradona" of surgical pathology, played a role not only as a diagnostician but also as a researcher, a consultant and a teacher, distinguishing himself as a real icon at all levels of modern pathology. He was an innovative promoter of emerging technologies including digital pathology.These few lines commemorate the digital side of the "Maestro" Juan Rosai from a junior's perspective highlighting how Rosai supported digital pathology and remembering that, according to his own words, digital pathology "will revolutionize the field of pathology, if it is not doing that already".
Applying Convolutional Neural Networks to Neuroimaging Classification Tasks: A Practical Guide in Python

In this chapter, we describe the process of obtaining medical imaging data and its storage protocol. The authors also explain in a step-by-step approach how to extract and prepare the medical imaging data for machine learning algorithms. And finally, the process of building and assessing a convolutional neural network for medical imaging data is illustrated.
An infinite, two-parameter family of polynomials with factorization similar to <i>X</i><sup><i>m</i></sup> − 1

For a suitable irreducible base polynomial f(x)∈Z[x] of degree k, a family of polynomials Fm(x) depending on f(x) is constructed with the properties:there is exactly one irreducible factor Φd,f(x) for Fm(x) for each divisor d of m;deg(Φd,f(x))=φ(d)·deg(f) generalizing the factorization of xm−1 into cyclotomic polynomials;when the base polynomial f(x)=x−1 this Fm(x) coincides with xm−1.As an application, irreducible polynomials of degree 12, 24, 24 are constructed having Galois groups of order matching their degrees and isomorphic to S3⊕C2,S3⊕C2⊕C2 and S3⊕C4, respectively.
Neural Complexity Assessment: A Deep Learning Approach to Readability Classification for European Portuguese Corpora

The following paper describes a Deep Learning model capable of classifying the inherent readability complexity of a piece of text in European Portuguese. The model was developed using modern Natural Language Processing techniques, featuring a highly-fine-tuned Neural Network which takes as input both the text as well as multiple metrics relating to it. This classifier was trained on a dataset featuring texts divided in 5 CEFR categories, obtaining an accuracy of 73%, a top 2 accuracy of 90% and an adjacent accuracy of 94%.
A Text Mining using Web Scraping for Meaningful Insights

Abstract This research involves the usage of Machine Learning technology and Natural Language Processing (NLP) along with the Natural Language Tool-Kit (NLTK). This helps develop a logical Text Summarization tool, which uses the Extractive approach to generate an accurate and a fluent summary. The aim of this tool is to efficiently extract a concise and a coherent version, having only the main needed outline points from the long text or the input document avoiding any type of repetitions of the same text or information that has already been mentioned earlier in the text. The text to be summarized can be inherited from the web using the process of web scraping or entering the textual data manually on the platform i.e., the tool. The summarization process can be quite beneficial for the users as these long texts, needs to be shortened to help them to refer to the input quickly and understand points that might be out of their scope to understand.
From “Onion Not Found” to Guard Discovery

Abstract We present a novel web-based attack that identifies a Tor user’s guard in a matter of seconds. Our attack is low-cost, fast, and stealthy. It requires only a moderate amount of resources and can be deployed by website owners, third-party script providers, and malicious exits—if the website traffic is unencrypted. The attack works by injecting resources from non-existing onion service addresses into a webpage. Upon visiting the attack webpage with Tor Browser, the victim’s Tor client creates many circuits to look up the non-existing addresses. This allows middle relays controlled by the adversary to detect the distinctive traffic pattern of the “404 Not Found” lookups and identify the victim’s guard. We evaluate our attack with extensive simulations and live Tor network measurements, taking a range of victim machine, network, and geolocation configurations into account. We find that an adversary running a small number of HSDirs and providing 5 % of Tor’s relay bandwidth needs 12.06 seconds to identify the guards of 50 % of the victims, while it takes 22.01 seconds to discover 90 % of the victims’ guards. Finally, we evaluate a set of countermeasures against our attack including a defense that we develop based on a token bucket and the recently proposed Vanguards-lite defense in Tor.
The Weighted Bitcoin Lightning Network

The Bitcoin Lightning Network (BLN) was launched in 2018 to scale up the number of transactions between Bitcoin owners. Although several contributions concerning the analysis of the BLN binary structure have recently appeared in the literature, the properties of its weighted counterpart are still largely unknown. The present contribution aims at filling this gap, by considering the BLN over a period of 18 months and focusing on its weighted, daily snapshot representation. As the study of the BLN weighted structural properties reveals, it is becoming increasingly ‘centralised’ at different levels, just as its binary counterpart: 1) the Nakamoto coefficient shows that the percentage of nodes whose degrees/strengths ‘enclose’ the 51% of the total number of links/total weight is rapidly decreasing; 2) the Gini coefficient confirms that several weighted centrality measures are becoming increasingly unevenly distributed; 3) the weighted BLN topology is becoming increasingly compatible with a core-periphery structure, with the largest nodes ‘by strength’ constituting the core of such a network. Further inspection of the resilience of the weighted BLN shows that removing such hubs leads to the network fragmentation into many components, an evidence indicating potential security threats - as the ones represented by the so called ‘split attacks’.
River Channel Extraction in SAR Images Using Level Sets Driven by Symmetric Kullback–Leibler Distance

This article proposes a novel level set method (LSM) that improves river channel extraction accuracy in synthetic aperture radar (SAR) images by developing global median image fitting energies. First, we define a new global median fitting image (GMFI) to approximate the input image and use this GMFI to construct the fitting energy based on the symmetric Kullback–Leibler distance (SKL). Second, to exploit more image grayscale features, a squared global median fitting image (SGMFI) is derived and another fitting energy is similarly constructed using this SGMFI based on SKL. Third, we integrate the above two fitting energies and introduce additional regularized energies. The proposed LSM is verified and compared with several state-of-the-art methods on real SAR images. The river channel extraction results indicate that our proposed LSM has a clear advantage in accuracy and is robust to level set initialization.
The Integration of Blockchain and Supply Chain Management: A state-of-the-art literature review

This study objective was to investigate the drivers and benefit associated with the blockchain-supply chain. The study followed the SLR (systematic literature review) approach to analyze and synthesize the extant literature on the integration of blockchain technology with the supply chain. The review analyzed 48 articles between 2013 to 2019 in international journals. The results indicate that blockchain technology is in its early stages in different industries, while the scholars are not fully aware of the potential and benefit associated with the blockchain technology. The limitations of the study and future research gap have been discussed at the end of the article.
Automata theory approach to predicate intuitionistic logic

Abstract Predicate intuitionistic logic is a well-established fragment of dependent types. Proof construction in this logic, as the Curry–Howard isomorphism states, is the process of program synthesis. We present automata that can handle proof construction and program synthesis in full intuitionistic first-order logic. Given a formula, we can construct an automaton such that the formula is provable if and only if the automaton has an accepting run. As further research, this construction makes it possible to discuss formal languages of proofs or programs, the closure properties of the automata and their connections with the traditional logical connectives.
Student online learning behavior management system based on deep data mining

In recent years, the emergence of cloud computing, Internet of things, big data, artificial intelligence, mobile Internet, and other modern information technologies has boosted the innovation of education and teaching methods and means, and many online teaching platforms or systems have emerged. The application of these platforms has provided space-time convenience for teaching and learning. However, due to the separation of teachers and students in space-time in online teaching mode, there are many problems in online teaching, It makes it difficult for teachers to supervise and manage learners' learning behavior, and make a real evaluation of students' learning efficiency. Based on the analysis of the characteristics of online students' learning behavior, this paper deeply analyzes the dilemma of online learning behavior management, and puts forward a management system for students' online learning behavior.
Capitalizing on Developer-Tester Communication – A Case Study

Communication between developers and testers can be a rich source of insights into software development processes and practices, which may not be easily discoverable from other means like retrospectives or project roadmaps. With the objective of deriving and capitalizing on potential development-related insights, we analyzed developer-tester communication in an industrial setting. We conducted a case study at a software-intensive Agile company, within the context of the development of one of their flagship products from 2016 to 2018. We applied Latent Dirichlet Allocation (LDA) to analyze communication between developers and testers, and then invited two case-company practitioners to study the results for insights into their developments processes: The findings reveal the case company’s efforts to improve their product stability, the growing emphasis on addressing end-user concerns and other quality-related issues. The practitioners interpreted these findings as indicators of evolution in their development process. Based on these findings and the state of the art, we propose an insight classification to highlight insights discoverable from developer-tester communication: Recognizing LDA’s potential for deriving insights, the practitioners are keen on incorporating it into their software development practices. The findings from this study serve as evidence for use and benefits of text-mining techniques like LDA in industrial setting, which other practitioners could adapt to elicit their own context-influenced insights. Furthermore, the insight classification can serve as a foundation for further investigation into the extent and type of insights discoverable from developer-tester communication.
Mining User’s Opinion Towards the Rising and Falling Trends of the Stock Market: A Hybrid Model

Mining users’ opinions towards the rising and falling trends of the stocks may help the management department estimate the risk and make timely decision. Existing methods ignore the effective fusion of domain information and pre-trained language models, hindering mining implicit semantic information. This paper proposes a hybrid method that adopts masked language modeling to obtain a domain-information-enhanced language model. Firstly, it generates an attention-mechanism-oriented masking based on words’ importance, word-level polarity and terminology. Then, the masked words and their corresponding knowledge are predicted to acquire domain-aware language representation. Experimental results on two public financial sentiment analysis datasets show the efficacy of the proposed model.
Identification of Legislative Errors Through Knowledge Representation and Interpretive Argumentation

High-quality legislative drafting requires not only linguistic fluency, but also in-depth legal expertise. In this paper we focus on the second problem and we develop a frame-based, semi-formal model useful in identification and discussion of potential legislative errors. We present a partial systematization of (potential) legislative errors in terms of the types of expert knowledge necessary to ascertain them. The systematization is based on the anticipated interpretation of the statutory text.
Privacy-preserving of patients with Differential Privacy: an experimental evaluation in COVID-19 dataset

The pandemic of the new coronavirus (COVID-19) has brought new challenges to health systems in almost every corner of the world, many of them overburdened. The data analysis has given support in the fight against the coronavirus. Through this analysis, government authorities, together with health care providers, adopted effective strategies. Yet, those strategies can not be careless of privacy concerns. The individuals’ privacy is a right of each citizen. Privacy techniques guarantee the analysis of health data without exposing individuals’ private information. However, a balance between data privacy and utility is essential for a good analysis of the data. This work will demonstrate that it is possible to guarantee the privacy of infected patients and maintain the utility of the data, allowing a sound analysis on them, from the visualization of the application of differentially private mechanisms on queries in the data of patients tested in the State of Ceará - Brazil.
GAN Image Generation and Detection Technology Based on Fractal Dimension

Abstract With the development of modern technology today, image analysis technology has become an important research direction in the computer field. Computer image processing technology is a cross and fusion involving multiple disciplines, which includes multiple research directions such as physics, chemistry, and computer science. Starting from the fractal dimension, this article studies the image generation and detection technology. The purpose of the research topic in this article is to have a deep understanding of image generation technology so as to master image processing methods proficiently. The methods used in this article include case analysis, data, comparison, and experimental methods, etc., and carry out related researches on image generation detection technology and fractal dimension. The experimental results show that the algorithm accuracy of GAN in the detection technology of computer-generated images is higher than other algorithms, and the improvement space is about 0.06.
A study of task scheduling algorithms in cloud computing

Cloud computing is the provision of information technology (IT) services including servers, storage, databases, network management, etc. Like any new technology, cloud computing requires many improvements and the establishment of precise standards to avoid risks. Task scheduling can be seen as the management and handling of a set of tasks from their start to the step of execution. Scheduling is a negotiation mechanism between two objects, one representing the user (or the ap-plication) and the other the resources. In cloud computing, Task scheduling is of-ten considered as a real challenge to managers. In this paper, we will present some concepts and research papers that have proposed improvements or solutions to this challenge and we will compare some tasks scheduling algorithms in the CloudSim simulator.
Classification of Tree Species by Trunk Image Using Conventional Neural Network and Augmentation of the Training Sample Using a Telegram-Bot

This paper considers the problem of creating a model of a convolutional neural network for recognizing tree species from the image of a trunk for ground-based lidar taxation of forest stands. To increase the probability of recognition, it is proposed to use a telegram bot for augmentation of the training set. Training, selection and comparison of convolutional neural network models was performed. A telegram bot has been created that allows you to automate the collection of images of the training sample. The study opens a cycle of works on modeling the carbon balance of forest plantations.
Sketches by MoSSaRT: Representative selection from manifolds with gross sparse corruptions

Conventional sampling techniques fall short of selecting representatives that encode the underlying conformation of non-linear manifolds. The problem is exacerbated if the data is contaminated with gross sparse corruptions. In this paper, we present a data selection approach, dubbed MoSSaRT, which draws robust and descriptive sketches of grossly corrupted manifold structures. Built upon an explicit randomized transformation, we obtain a judiciously designed representation of the data relations, which facilitates a versatile selection approach accounting for robustness to gross corruption, descriptiveness and novelty of the chosen representatives, simultaneously. Our model lends itself to a convex formulation with an efficient parallelizable algorithm, which coupled with our randomized matrix structures gives rise to a highly scalable implementation. Theoretical analysis guarantees probabilistic convergence of the approximate function to the desired objective function and reveals insightful geometrical characterization of the chosen representatives. Finally, MoSSaRT substantially outperforms the state-of-the-art algorithms as demonstrated by experiments conducted on both real and synthetic data.
A Visualization Method for Scientific Research Data of University Teachers Based on Temporal Hierarchical Layout Strategy

Scientific research evaluation has become the core of scientific research and academic management in colleges and universities. According to the scientific research data of university teachers in school, the objective and accurate scientific research evaluation of individual teachers and even the whole university will play a guiding role in the scientific research planning of university teachers and the reasonable allocation of scientific research resources. According to the characteristics of teachers' scientific research data with both complex multidimensional and temporal attributes, a hybrid visualization method (Zoomable Sunburst Time-Line Mix) was proposed based on hierarchical zoom nesting map and temporal representation. The method is applied to teachers' research data, and the visualization of teachers and the whole university is realized. The experimental results show that the proposed ZSTM method can accurately display the scientific research data of teachers, and help users at all levels of universities to effectively and intuitively evaluate the scientific research management work.
A Method Using Quantum Genetic Algorithm optimized BP Neural Network of Real-time Fault Detection for the Liquid Rocket Engine

As an essential part of modern launch vehicles, liquid rocket engines have attracted more and more attention for their safe operation and reusable capability. Aiming at the real-time fault detection requirements of liquid rocket engines, this article proposes a real-time fault detection method for liquid rocket engines using quantum genetic algorithm optimizing BP neural network. First, use quantum genetic algorithm to optimize the BP neural network to obtain the QGABP model, and then use the model to predict the sensor value. After that, the threshold is used to judge whether the engine is faulty. According to the comparison and simulation results through different models, this method accelerates the convergence speed, can converge to the global optimal solution faster, realize the real-time fault detection of liquid rocket engines, and has engineering application merits.
Unscented Kalman filter for long-distance vessel tracking in geodetic coordinates

This paper describes a unique instantiation of the unscented Kalman filter (UKF) that is particularly well-suited to long-distance surface vessel tracking. The proposed filter leverages a nonlinear process model to predict the position, speed, and heading of targets directly in geodetic coordinates, obviating the need for intermediate coordinate frame transformations, and enabling the use of a simple linear observer. These features differentiate the proposed filter from prior architectures, most of which require the definition of a planar coordinate system in order to describe the linearized state kinematics in an easily differentiable form. Reliance upon local Earth-tangent frames is acceptable in confined operating regions, but incurs substantial estimation error when the target is far from the local origin. It is shown that this error grows with distance-cubed from the local origin. By tracking targets directly in geodetic coordinates, the proposed filter is able to achieve more consistent performance, and a three-fold reduction in computational cost. A series of numerical simulations and field exercises are performed to ground these claims, and verify the efficacy of the proposed filter.