Reviewerly/exHarmony huggingface dataset:
An Expert Knowledge Generation Model in Smart Contract Vulnerability Fuzzing

With the development of smart contracts, the complexity of smart contracts continues to increase. Vulnerabilities may be hidden in complex contracts, which brings great hidden dangers to the development of contracts. Many fuzzing methods are used to detect contract vulnerabilities. Fuzzing requires expert knowledge as a rule for vulnerability detection. Expert knowledge depends on the induction of professionals, which lags behind the development of vulnerabilities. Although there are some methods using neural network classification models to solve the problem of expert knowledge generation, they do not consider the challenges brought by global variables. Global variables may carry dangerous data, which indirectly leads to vulnerabilities. The existing expert knowledge model does not analyze the semantics of global variables. To address this issue, we propose a model based on transaction bytecode and global variable semantics. We build a dynamic taint analysis model to capture the semantics of global variables. By capturing the global semantics, we solve the problem that global variables poses for expert knowledge generation models. We experimentally compare models with and without global variable semantics. Experiments show that our method is able to detect more vulnerabilities.
Empirical Analysis of Sampling Methods on Imbalanced Data

With imbalanced data being one of the significant drawbacks encountered while evaluating the effectiveness of machine learning models, where the distribution of instances within a dataset is skewed, we aim to provide data-level solutions to manipulate the balance of data before feeding them onto classifiers. The decline in performance in such datasets occurs when the accuracy in minority classes is poor due to very few instances available in them to train the model. In this paper, we assess various existing sampling techniques in order to handle skewed data to enhance the classification performance of the model which includes traditional Oversampling and Undersampling and five variants of Smote: Smote, Borderline Smote, SVM Smote, Adasyn, and SmoteTomek. We have experimented with these methods on a variety of imbalanced binary classification datasets ranging from highly imbalanced to moderately imbalanced and evaluated them based on performance metrics such as recall, precision, and F-measure.
The Creation Strategies, Participants’ Satisfaction and Learning Experiences of ‘Sure and Share’s Fact Check Detective Club’

‘Sure and Share’s Fact Check Detective Club’ is a project initiated by the Sure and Share Center, Thailand’s well-established fact-checking center under Thai News Agency. Due to the COVID-19 pandemic alarming situation during 2021-2022, the project with the main goal to enhance fact-checking knowledge and skills for 651 participants nationwide, aged 12-87 years old, had
An Investigation of the Impacts of Available Real Single-photon Sources on Quantum Communication Secure Length

Abstract: One of the most significant applications of single-photon sources is secure quantum communication. In this research, the security of the most famous protocol of quantum key distribution (BB84) has been studied by considering the characteristics of a single-photon source. This paper derives the impact of propagation on photon statistics. The result reveals that the propagation leads to a change in its value, especially in long distances. Besides, the parameter deviation from an ideal source causes a decrease in the confident length. We achieved a difference of up to 1800 km for the safe distance by studying the relation for minimum transmission coefficient in available single-photon sources. Keywords: Quantum communication, Quantum key distribution, Security, Single-photon source.
Hands-free Mobile Device Control Through Head Pose Estimation

Despite touch input being the predominant way to interact with mobile devices, there are many situations where alternative forms of input are required. Touch input is not only problematic for users with motor impairments, but also inconvenient when the hands are oily from cooking or needed to hold something. Existing alternatives like voice input or hand gestures only mitigate some of the problems. They raise privacy concerns when others can overhear the voice commands and are compromised by inaccurate sensor readings in highly dynamic mobile contexts. In silent environments like libraries, voice input is not an option either. In this paper, we therefore design and implement RUDoLF, a robust control interface for mobile devices based on head poses. Users can turn their head in four directions to control applications. To classify head poses, we extract facial landmarks from video frames captured with the front facing camera. Our evaluation with three mobile applications validates RUDoLF as a robust and easy-to-use control interface that outperforms previous approaches in speed, accuracy, and usability.
Challenges and Solutions in Data Modeling Relating to Authority Records for Named Objects at the National Library of Latvia

The article presents challenges and new solutions to the coding of authority records for named objects, such as historical, public, or religious buildings, ships, etc., at the National Library of Latvia. The paper also outlines the main reasons for separating the named objects from the corporate body entity historically recorded as such following Library of Congress practice. It describes how various named objects from the Library of Congress are changed to topical headings for diverse types of information resources, such as posters, photographs, videos, and other materials, at the National Library of Latvia. It includes conclusions about the challenges and benefits.
A Node Differential Privacy-Based Method to Preserve Directed Graphs in Wireless Mobile Networks

With the widespread popularity of Wireless Mobile Networks (WMNs) in our daily life, the huge risk to disclose personal privacy of massive graph structure data in WMNs receives more and more attention. Particularly, as a special type of graph data in WMNs, the directed graph contains an amount of sensitive personal information. To provide secure and reliable privacy preservation for directed graphs in WMNs, we develop a node differential privacy-based method, which combines differential privacy with graph modification. In the method, the original directed graph is first divided into several sub-graphs after it is transformed into a weighted graph. Then, in each sub-graph, the node degree sequences are obtained by using an exponential mechanism and micro-aggregation is adopted to get the noised node degree sequences, which is used to generate a synthetic directed sub-graph through edge modification. Finally, all synthetic sub-graphs are merged into a synthetic directed graph that can preserve the original directed graph. The theoretical analysis proves that the proposed method satisfies differential privacy. The results of the experiments demonstrate the effectiveness of the presented method in privacy preservation and data utility.
Study on fast algorithm of neutron radiation field under complex terrain scenario based on ensemble learning approach

In this study a prediction algorithm has been proposed to rapidly figure out neutron radiation field for nuclear explosion under complex terrain scenario based on ensemble learning approach, which could be an impossibility for traditional radiation transport simulation methodology. By analyzing the influence of complex surface morphology on the radiation field, a series of characteristic parameters which could characterize the topographic features and their influence on the transport of neutrons and secondary gamma in the atmosphere have been extracted with the application of DEM, and the sample sethas been constructedwith the MC simulation results of terrain samples generated by random algorithm, to be used to train the prediction model for the neutron radiation field of nuclear explosion. In order to verify the actual prediction performance of the model, the study has implemented the prediction for the neutron flux, neutron tissue dose and secondary gamma tissue dose under the authentic urban and mountainous terrain scenarios, and analyzed and compared the results from fast prediction and MC simulation in different evaluation dimensions. The comparisons suggest that both of the results are in good agreement with each other, demonstrating that the fast prediction models preliminarily possess the engineering application value. In addition, a feasible approach to improve the generalization performance of the prediction model for various radiation scenarios has been proposed, which could be deemed as a reference for further research.
Software vulnerability prediction: A systematic mapping study

Software security is considered a major aspect of software quality as the number of discovered vulnerabilities in software products is growing. Vulnerability prediction is a mechanism that helps engineers to prioritize their inspection efforts focusing on vulnerable parts. Despite the recent advancements, current literature lacks a systematic mapping study on vulnerability prediction. This paper aims to analyze the state-of-the-art of vulnerability prediction focusing on: (a) the goals of vulnerability prediction-related studies; (b) the data collection processes and the types of datasets that exist in the literature; (c) the mostly examined techniques for the construction of the prediction models and their input features; and (d) the utilized evaluation techniques. We collected 180 primary studies following a broad search methodology across four popular digital libraries. We mapped these studies to the variables of interest and we identified trends and relationships between the studies. The main findings suggest that: (i) there are two major study types, prediction of vulnerable software components and forecasting of the evolution of vulnerabilities in software; (ii) most studies construct their own vulnerability-related dataset retrieving information from vulnerability databases for real-world software; (iii) there is a growing interest for deep learning models along with a trend on textual source code representation; and (iv) F1-score was found to be the most widely used evaluation metric. The results of our study indicate that there are several open challenges in the domain of vulnerability prediction. One of the major conclusions, is the fact that most studies focus on within-project prediction, neglecting the real-world scenario of cross-project prediction.
Maximizing Efficiency in Marketing Planning: Artificial Neural Network Regression and Data Imputation for Improving Business Forecasting

Marketing planning plays an important role in the success of any business in general. The ability to predict future earnings tends to allow us to make effective decisions and plan actions. These will help us work more smoothly in the future. However, traditional methods such as Linear Regression may limit the accuracy of predictions for various reasons. To fix this problem, we propose neural network regression with enhanced pseudo-input, working in tandem with business-predictive data models to fill in the missing information. The proposed approach involves training the model using datasets from 3 and 7 days with some missing data through deep learning regression to obtain more accurate prediction results. Comparing our proposed method with the classical linear regression method, our proposed method provided us with higher performance as evidenced by reduced losses.
Improved DE-MC Algorithm with Automated Outliers Detection

The DE-MC algorithm joins two approaches: the differential evolution and the theory of the Markov chains. This population MCMC method aims to improve the numerical effectiveness and the convergence speed of the Metropolis-Hastings algorithm. In this paper, we equip this standard approach with different unsupervised and automated methods for outlier detection and replacement. As our numerical experiments suggest, the obtained DE-MC-out algorithm convergences faster and produces output samples that are closer to the desired target density than the DE-MC method without the increased timing.
Universal regression with adversarial responses

We provide algorithms for regression with adversarial responses under large classes of non-i.i.d. instance sequences, on general separable metric spaces, with provably minimal assumptions. We also give characterizations of learnability in this regression context. We consider universal consistency, which asks for strong consistency of a learner without restrictions on the value responses. Our analysis shows that such an objective is achievable for a significantly larger class of instance sequences than stationary processes, and unveils a fundamental dichotomy between value spaces: whether finite-horizon mean estimation is achievable or not. We further provide optimistically universal learning rules, that is, such that if they fail to achieve universal consistency, any other algorithms will fail as well. For unbounded losses, we propose a mild integrability condition under which there exist algorithms for adversarial regression under large classes of non-i.i.d. instance sequences. In addition, our analysis also provides a learning rule for mean estimation in general metric spaces that is consistent under adversarial responses without any moment conditions on the sequence, a result of independent interest.
Construction of C1-Smooth Piecewise Quadratic Functions for Solving Boundary Value Problems of Fourth-Order Equations on a Triangular Grid

In this paper, we present one approach to constructing continuously differentiable piecewise-quadratic functions on a triangular grid, based on smoothing a piecewise-linear function in the vicinity of edges and triangulation nodes. In the works [8], [7], the issues of approximation of the functional (1) in triangular grids and the convergence of the variational method for solving the boundary value problem of the equation (2) were studied. However, in the numerical solution there are difficulties associated with the construction of continuously differentiable piecewise polynomial functions on triangulations. In particular, their construction requires solving large systems of equations at each step of the variational method. When trying to get by with only continuous piecewise polynomial functions, we got a negative result (divergence of approximate solutions was found) [9]. In this paper, we circumvent the difficulties that arise — we indicate a method for constructing piecewise polynomial functions that have continuous partial derivatives. With the help of this class of functions, we have obtained formulas for approximating the functional (1) and tested the method on the example of a biharmonic equation.
TEACHERS’ WORKSHOPS AS QUALIFICATION FORM – ACTIVE LEARNING, SHARING, MOTIVATION

The article presents a concrete realization of a workshop for teachers on the topic „STEM STEAM concept in education – practical skills and methodological solutions“. The need for quality pedagogical qualification when placing the teacher in the active position of a learner is justified. A model of work is proposed where practical work, based on project-based approach is combined with individual and teamwork of teachers. Emphasis is placed on the competencies that must be formed and developed in the nowadays teachers to ensure objective conditions for the realization of an effective learning process.
Multi-View Azimuth Stereo via Tangent Space Consistency

We present a method for 3D reconstruction only using calibrated multi-view surface azimuth maps. Our method, multi-view azimuth stereo, is effective for textureless or specular surfaces, which are difficult for conventional multi-view stereo methods. We introduce the concept of tangent space consistency: Multi-view azimuth observations of a surface point should be lifted to the same tangent space. Leveraging this consistency, we recover the shape by optimizing a neural implicit surface representation. Our method harnesses the robust azimuth estimation capabilities of photometric stereo methods or polarization imaging while bypassing potentially complex zenith angle estimation. Experiments using azimuth maps from various sources validate the accurate shape recovery with our method, even without zenith angles.
Introducing Virtual Thematic Issues for <i>Chemical Reviews</i>

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTIntroducing Virtual Thematic Issues for Chemical ReviewsSharon Hammes-SchifferSharon Hammes-SchifferMore by Sharon Hammes-Schifferhttps://orcid.org/0000-0002-3782-6995Cite this: Chem. Rev. 2023, 123, 16, 9719Publication Date (Web):August 23, 2023Publication History Received20 July 2023Published online23 August 2023Published inissue 23 August 2023https://doi.org/10.1021/acs.chemrev.3c00518Copyright © 2023 American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views405Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (921 KB) Get e-AlertscloseSUBJECTS:Biomaterials,Biotechnology,Catalysis,Metamaterials,Self healing materials Get e-Alerts
Bilateral form characterization of curved transparent components using chromatic confocal sensor

To accurately characterize the geometric form of key components in industrial products, many profile measuring instruments have been developed to date. However, there are some special components, such as the hemispherical resonators featuring thin curved transparent surfaces, which are difficult to be measured using general instruments. The chromatic confocal technology was introduced in this paper to conduct the bilateral form characterization of this type of surfaces. Based on the assumption of tilted plate approximation, a modified ray tracing model was presented to correct the errors produced by the curved surfaces. The experiment of measurement on tilted, fused silica plates was conducted to demonstrate the effectiveness of the proposed model. Results show that the developed model improves the measurement accuracy of plates with large tilt angle. A three-axis motion stage was built with self-developed chromatic confocal sensor to obtain the bilateral profiles of the resonator. By fitting the spheres with the scanning profiles, the form error between the measurement points and the fitted spheres can be calculated. Partial form errors along the circumferential and axial directions of the resonator are evaluated, and an intuitive assessment of its thickness variation along the circumferential direction was made.
Cybersecurity for Heat Treaters

Abstract This article describes recent U.S. government cybersecurity regulations and their applicability to the heat treating industry. Compliance mitigates the impact of lost or compromised data, secures sensitive information, helps maintain a trustworthy reputation with customers, and helps to avoid the ensuing legal trouble that comes after a cybersecurity breach.
IoT-based system for intellectual property protection in social media using blockchain

Since the creation and analysis of such data are essential to the functioning of the IoT, they must be safeguarded throughout the extended device life span. Due to the fact that cryptocurrency mining introduced blockchain technology, which is its primary technology, to the world. But it now heavily involves the Internet of Things (IoT). Scalability, privacy, and confidence in IoT security are issues that blockchain technology is the ideal ally to address.The purpose of this chapter is to investigate trends in the adoption of blockchain-related approaches and advancements in an IoT environment. The curiosities that go along with this chapter are as follows, as for related work: a brief introduction of blockchain is given, followed by a description of its benefits. Additionally, we look into the underlying concerns that the examination network has with the seamless integration of BC and IoT and highlight the key unresolved problems and directions for future research. In the final section of this chapter, we also provide a prospective use case for blockchain and IoT.
Blockchain technologies in healthcare social media computing: trends and opportunities

Social media has influenced almost all computing domains including information communication. The most benefited areas are entrepreneurs, startups, and other business domains. Social media has not only transformed the Business, rather, sectors such as healthcare are equally benefited to a large extent. The "idea generation" by intellectuals (active in social sites) is a fundamental source of social media to address enormous issues of the society. Nevertheless, having a tremendous potential for social media this field is not harnessed up to the maximum extent due to the limitations imposed in terms of identity fraud, misinformation, and fake news. To address the above breaches, blockchain technology which is well established in the computing domain would be beneficial to a large extent. Blockchain technology which is known for transparency, ensuring confidentiality, and security have scope to alleviate the capabilities of social media computing.
Application and Appreciation of Fantasy Literature in Liberal Arts Education - Focusing on Where ever It is and Nakseomunhaksa

This paper presented the basis for fantasy literature to be a new alternative to literary and liberal arts education at universities, and presented the educational values and utilization measures of Lee Seung-woo's 『Where ever It is』 and Kim Jong-kwang's 『Nakseomunhaksa』. The theory of fantasy and fantasy literature (fantasy etymology and definition, how to classify the subject and conditions of fantasy literature, and the creative principle of fantasy novels) were introduced using related books and applied to the appreciation of fantasy literature. In particular, the symbolic meanings of Lee Seung-woo's 『Where ever It is』 and Kim Jong-kwang's 『Nakseomunhaksa』 such as "Seori," "Cave," and "Yulyeoguk" were identified, and academic principles applied to each material were found. Based on the principle, the unique theme of the work was identified, the relationship between fantasy, and the absurd reality in the work, and the relationship between characters' desires and frustrations with fantasy, and the basis for applying it to literary liberal arts education.