Reviewerly/exHarmony huggingface dataset:
To the evaluation of the parameters of the regression equation between the radiometric and geological testing

Abstract Comparisons of the regression equation between two methods for testing rocks and ores at the Dukat gold-silver deposit are made using two algorithms. In the first loss function defined by the sum of squares of residuals (deviations from the values of the dependent variable of the regression line), in the second - the sum of the absolute values of the residual normalized to sum values of the dependent and independent variables. As a result, it was shown that in the first case, in order to increase the accuracy of estimates of the coefficients, it is necessary to exclude observations in which the absolute value of the residuals normalized to their standard deviation exceeds the critical level of 5-7. In the second case, it is stable to sample formation and the number of observations can be reduced by 2-3 times. Under these conditions, the error in the estimates of the coefficients turns out to be close.
Improved Simplified Particle Swarm Optimization Based on Piecewise Nonlinear Acceleration Coefficients and Mean Differential Mutation Strategy

Particle swarm optimization (PSO) has been widely used in various optimization fields because of its easy implementation and high efficiency. However, it suffers from some limitations like slow convergence and premature convergence when solving high-dimensional optimization problems. This paper attempts to address these open issues. Firstly, a new method of parameter adjustment named piecewise nonlinear acceleration coefficients is introduced to the simplified particle swarm optimization algorithm (SPSO), and an improved algorithm called piecewise-nonlinear-acceleration-coefficients-based SPSO (P-SPSO) is proposed. Then, a mean differential mutation strategy is developed for the update mechanism of P-SPSO, and another improved algorithm named mean-differential-mutation-strategy embedded P-SPSO (MP-SPSO) is proposed. To validate the performance of the proposed algorithms, four different sets of experiments are carried out in this paper. The results show that, 1) the proposed P-SPSO can get better solutions than other four classic improved SPSO with different acceleration coefficients, 2) the proposed MP-SPSO algorithm shows better optimization performance than P-SPSO and mean-differential-mutation-strategy-based SPSO (M-SPSO), 3) the proposed MP-SPSO is clearly seen to be more successful than other eight well-known PSO variants, 4) compared to other nine intelligent optimization algorithms, MP-SPSO achieves better performance in terms of solution quality and robustness. Moreover, the proposed MP-SPSO algorithm is successfully applied to a real constrained engineering problem and provides better solutions than other methods.
Digitalization of Higher Education and Professional Development of Educators: Technologies and New Opportunities

Nowadays, the development of the digital environment and the global network covers almost all spheres of life. That is why special attention is focused on the provision of the educational process with appropriate teaching aids and curricula, so that schools, universities, educators, and professional development of educators meet modern national and global challenges and provide support in the field of digital technologies, developing and shaping modern digital competencies. The article presents an analysis of digitalization in higher education and the necessity to improve the professional development of higher school educators and professional development technologies related to this process. Based on an expert survey, educational trends in the era of educational digitalization, innovative pedagogical technologies, and teaching methods are determined. The results of the survey, related to the introduction of new pedagogical and digital technologies in the educational process, allow the authors to design a model for the professional development of educators in the direction of the digital transformation of the educational process.
Teacher Training for ‘Augmented Reading’: The Living Book Approach and Initial Results

Living Book—Augmenting Reading for Life, a three-year EU-funded Erasmus + project (September 2016–August 2019), exploited the affordances of augmented reality (AR) and other emerging technologies in order to address the underachievement of European youth in reading skills. The program developed an innovative approach that empowers teachers from upper primary and lower secondary schools (ages 9–15) to ‘augment’ students’ reading experiences through combining offline activities promoting reading literacy with online experiences of books’ ‘virtual augmentation’ and with social dynamics. Various professional learning activities were designed within the project, aimed at strengthening European teachers’ profile and competences in effectively integrating the Living Book approach into their classroom activities, and in dealing with diversified groups of learners, particularly pupils from disadvantaged backgrounds. Teachers also received training in how to involve parents, and particularly those from disadvantaged and/or migrant backgrounds, in proreading activities to back the overall Living Book strategy at home. The current article provides an overview of the main phases of the Living Book project implementation, and of the program’s key activities and outputs. It also outlines the content and structure of the ‘Augmented Teacher’ and ‘Augmented Parent-Trainer’ training courses developed within the project. Finally, it reports on the main insights gained from the pilot testing of the courses and the follow-up classroom experimentation that took place in the project partner countries.
Research Opportunities for E-health Applications with DNA Sequence Data using Blockchain Technology

E-health applications are health-related computing solutions that use the internet to provide services to users. One of the main challenges of E-health applications is the data privacy and security of patients. E-health application need should provide security and privacy mechanisms due to sensitive user data such as medical exams, DNA sequences, and others. Blockchain technology has become a research focus on several sectors that require their inherent characteristics, especially, about the security and privacy of data. This paper aims to present research opportunities with the use of Blockchain technology in applications that manipulate DNA sequence data. For this purpose, we propose an architecture for general E-health applications with Blockchain and implement a Proof of Concept (POC) to analyze the use of Blockchain technology in E-health applications using DNA sequence data. We also listed a set of research opportunities that permeate this topic.
Building Open Trusted Execution Environments

Trusted execution environments (TEEs) are a growing part of the security ecosystem. Unfortunately, widely available TEEs are hampered by closed designs and a lack of flexibility. We outline the challenges to TEEs, advocate for extensible and portable open TEEs, and detail current efforts.
Two factor authentication framework based on ethereum blockchain with dApp as token generation system instead of third-party on web application

Authentication is a method for securing an account by verifying the user identity by inputting email with a password. Two factor authentications is an authentication system that combines the first-factor authentication with the second factor. General two factor authentication by entering an email or username with a password are similar. However, two factor authentication requires additional information that must be inputted by the user. Additional information can be in the form of tokens or one-time passwords (OTP). Two factor authentications generally still uses third-party services to generate token or OTP still have vulnerable because can attacked from tokens steal through MITM and found that the generated tokens with the same value. Therefore, we propose a two-factor authentication framework based on ethereum blockchain with dApp as token generation system. Firstly, outcome from the analysis of the system, next succeeded in creating a two-factor authentication system without using third-parties. Second, token system generate up to 3164 different tokens in one second and has been collisions tested. Third, security method to protect token from MITM attack. The attacker unable to get access caused all the checking are done by dApp user authentication.
Optimal Planning Modulo Theories

We consider the problem of planning with arithmetic theories, and focus on generating optimal plans for numeric domains with constant and state-dependent action costs. Solving these problems efficiently requires a seamless integration between propositional and numeric reasoning. We propose a novel approach that leverages Optimization Modulo Theories (OMT) solvers to implement a domain-independent optimal theory-planner. We present a new encoding for optimal planning in this setting and we evaluate our approach using well-known, as well as new, numeric benchmarks.
Decentralized Corporate Governance via Blockchain Technology

Corporations and other forms of business organizations can be supplemented with blockchain-based agency constructs. Blockchain-based decentralized autonomous organizations (DAOs) expand the definition of the firm. On-chain DAO governance enables dynamic regulatory features that facilitate unprecedented decentralized regulatory solutions.
Do We Need Zero Training Loss After Achieving Zero Training Error

Overparameterized deep networks have the capacity to memorize training data with zero \emph{training error}. Even after memorization, the \emph{training loss} continues to approach zero, making the model overconfident and the test performance degraded. Since existing regularizers do not directly aim to avoid zero training loss, it is hard to tune their hyperparameters in order to maintain a fixed/preset level of training loss. We propose a direct solution called \emph{flooding} that intentionally prevents further reduction of the training loss when it reaches a reasonably small value, which we call the \emph{flood level}. Our approach makes the loss float around the flood level by doing mini-batched gradient descent as usual but gradient ascent if the training loss is below the flood level. This can be implemented with one line of code and is compatible with any stochastic optimizer and other regularizers. With flooding, the model will continue to random walk with the same non-zero training loss, and we expect it to drift into an area with a flat loss landscape that leads to better generalization. We experimentally show that flooding improves performance and, as a byproduct, induces a double descent curve of the test loss.
An Anomaly Comprehension Neural Network for Surveillance Videos on Terminal Devices

Anomaly comprehension in surveillance videos is more challenging than detection. This work introduces the design of a lightweight and fast anomaly comprehension neural network. For comprehension, a spatio-temporal LSTM model is developed based on the structured, tensorized time-series features extracted from surveillance videos. Deep compression of network size is achieved by tensorization and quantization for the implementation on terminal devices. Experiments on large-scale video anomaly dataset UCF-Crime demonstrate that the proposed network can achieve an impressive inference speed of 266 FPS on a GTX-1080Ti GPU, which is 4.29 faster than ConvLSTM-based method; a 3.34% AUC improvement with 5.55% accuracy niche versus the 3D-CNN based approach; and at least 15k× parameter reduction and 228× storage compression over the RNN-based approaches. Moreover, the proposed framework has been realized on an ARM-core based IOT board with only 2.4W power consumption.
Incentivising Participation in Liquid Democracy with Breadth-First Delegation

Liquid democracy allows members of an electorate to either directly vote over the available election alternatives, or delegate their voting rights to someone they trust. Most of the liquid democracy literature and implementations allow each voter to nominate only one delegate per election. However, if that delegate abstains, the voting rights assigned to her are left unused. To minimise the number of unused delegations, it has been suggested that each voter should declare a personal ranking over voters she trusts. In this paper, we show that even if personal rankings over voters are declared, the standard delegation method of liquid democracy remains problematic. More specifically, we show that when personal rankings over voters are declared, it could be undesirable to receive delegated voting rights, which is contrary to what liquid democracy fundamentally relies on. To solve this issue, we propose a new method to delegate voting rights in an election, called breadth-first delegation. Additionally, the proposed method prioritises assigning voting rights to individuals closely connected to the voters who delegate.
Infinitely deep neural networks as diffusion processes

When the parameters are independently and identically distributed (initialized) neural networks exhibit undesirable properties that emerge as the number of layers increases, e.g. a vanishing dependency on the input and a concentration on restrictive families of functions including constant functions. We consider parameter distributions that shrink as the number of layers increases in order to recover well-behaved stochastic processes in the limit of infinite depth. This leads to set forth a link between infinitely deep residual networks and solutions to stochastic differential equations, i.e. diffusion processes. We show that these limiting processes do not suffer from the aforementioned issues and investigate their properties.
AutoGraph: Imperative-style Coding with Graph-based Performance

There is a perceived trade-off between machine learning code that is easy to write, and machine learning code that is scalable or fast to execute. In machine learning, imperative style libraries like Autograd and PyTorch are easy to write, but suffer from high interpretive overhead and are not easily deployable in production or mobile settings. Graph-based libraries like TensorFlow and Theano benefit from whole-program optimization and can be deployed broadly, but make expressing complex models more cumbersome. We describe how the use of staged programming in Python, via source code transformation, offers a midpoint between these two library design patterns, capturing the benefits of both. A key insight is to delay all type-dependent decisions until runtime, via dynamic dispatch. We instantiate these principles in AutoGraph, a software system that improves the programming experience of the TensorFlow library, and demonstrate usability improvements with no loss in performance compared to native TensorFlow graphs. We also show that our system is backend agnostic, and demonstrate targeting an alternate IR with characteristics not found in TensorFlow graphs.
Localization Uncertainty Estimation for Anchor-Free Object Detection

Since many safety-critical systems, such as surgical robots and autonomous driving cars operate in unstable environments with sensor noise and incomplete data, it is desirable for object detectors to take the localization uncertainty into account. However, there are several limitations of the existing uncertainty estimation methods for anchor-based object detection. 1) They model the uncertainty of the heterogeneous object properties with different characteristics and scales, such as location (center point) and scale (width, height), which could be difficult to estimate. 2) They model box offsets as Gaussian distributions, which is not compatible with the ground truth bounding boxes that follow the Dirac delta distribution. 3) Since anchor-based methods are sensitive to anchor hyper-parameters, their localization uncertainty could also be highly sensitive to the choice of hyper-parameters. To tackle these limitations, we propose a new localization uncertainty estimation method called UAD for anchor-free object detection. Our method captures the uncertainty in four directions of box offsets (left, right, top, bottom) that are homogeneous, so that it can tell which direction is uncertain, and provide a quantitative value of uncertainty in [0, 1]. To enable such uncertainty estimation, we design a new uncertainty loss, negative power log-likelihood loss, to measure the localization uncertainty by weighting the likelihood loss by its IoU, which alleviates the model misspecification problem. Furthermore, we propose an uncertainty-aware focal loss for reflecting the estimated uncertainty to the classification score. Experimental results on COCO datasets demonstrate that our method significantly improves FCOS [32], by up to 1.8 points, without sacrificing computational efficiency. We hope that the proposed uncertainty estimation method can serve as a crucial component for the safety-critical object detection tasks.
Cluster Validation for Mixed-Type Data

For cluster analysis based on mixed-type data (i.e. data consisting of numerical and categorical variables), comparatively few clustering methods are available. One popular approach to deal with this kind of problems is an extension of the k-means algorithm (Huang, 1998), the so-called k-prototype algorithm, which is implemented in the R package clustMixType (Szepannek and Aschenbruck, 2019). It is further known that the selection of a suitable number of clusters k is particularly crucial in partitioning cluster procedures. Many implementations of cluster validation indices in R are not suitable for mixed-type data. This paper examines the transferability of validation indices, such as the Gamma index, Average Silhouette Width or Dunn index to mixed-type data. Furthermore, the R package clustMixType is extended by these indices and their application is demonstrated. Finally, the behaviour of the adapted indices is tested by a short simulation study using different data scenarios.
Continuous Diffusion Analysis

In this work, we propose a new technique called Continuous Diffusion Analysis (CDA) that can be used to study, design, and compare of cryptographic algorithms.CDA allows us to generalize cryptographic algorithms by transforming the discrete bits into probabilities such that the algorithm is generalized into a continuous mathematical function.We propose three new metrics to measure the diffusion in this generalized continuous space, namely the Continuous Avalanche Factor, the Continuous Neutrality Measure, and the Diffusion Factor.In addition, we show that these measures can be used to analyze the diffusion of cryptographic algorithms, in particular, the Diffusion Factor can be used to compare the diffusion without the need of reducing the number of rounds or considering a small subset of bits.To demonstrate the effectiveness of CDA, we also present a case study with the algorithms Salsa, Chacha, AES, and Speck.
Characterization of 3D bifurcations in micro-scan and MRA-TOF images of cerebral vasculature for prediction of intra-cranial aneurysms

An aneurysm is a vascular disorder where ballooning may form in a weakened section of the wall in the blood vessel. The swelling of the aneurysm may lead to its rupture. Intra-cranial aneurysms are the ones presenting the higher risks. If ruptured, the aneurysm may induce a subarachnoid haemorrhage which could lead to premature death or permanent disability. In this study, we are interested in locating and characterizing the bifurcations of the cerebral vascular tree. We use a 3D skeletonization combined with a graph-based approach to detect the bifurcations. In this work, we thus propose a full geometric characterisation of the bifurcations and related arteries. Aside from any genetic predisposition and environmental risk factors, the geometry of the brain vasculature may influence the chance of aneurysm formation. Among the main achievements, in this paper, we propose accurate, predictive 3D measurements of the bifurcations and we furthermore estimate the risk of occurrence of an aneurysm on a given bifurcation.
Adversarial Attacks on Content-Based Filtering Journal Recommender Systems

Recommender systems are very useful for people to explore what they really need. Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers. In order to improve the efficiency of selecting the most suitable journals for publishing their works, journal recommender systems (JRS) can automatically provide a small number of candidate journals based on key information such as the title and the abstract. However, users or journal owners may attack the system for their own purposes. In this paper, we discuss about the adversarial attacks against content-based filtering JRS. We propose both targeted attack method that makes some target journals appear more often in the system and non-targeted attack method that makes the system provide incorrect recommendations. We also conduct extensive experiments to validate the proposed methods. We hope this paper could help improve JRS by realizing the existence of such adversarial attacks.
Examining Human Individual Differences in Cyber Security and Possible Implications for Human-Machine Interface Design

With society now heavily invested in cyber-technology and most cyber-attacks due to human error, it has never been more vital to focus research on human-centric interventions. Whilst some studies have previously investigated the importance of end-user individual differences (gender, age, education, risk-taking preferences, decision-making style, personality and impulsivity) the current study extended the research to also include acceptance of the internet and the constructs used to explain behavior within the Theory of Planned Behavior (TPB) and Protection Motivation Theory (PMT). Seventy-one participants completed a battery of questionnaires on personality, risk-taking preferences, decision-making style, personality, impulsivity, acceptance of the internet, the combined PMT and TPB questionnaire, as well as an online cyber-security behaviors questionnaire. Gender, age and education did not relate to any cyber-security behaviors, however a number of individual differences were associated. These behaviors include financial risk-taking, avoidant decision-making plus ease of use, facilitating conditions, and trust in the internet. It was also found that safer cyber-security behaviors are seen in those who appraise threat as high, perceive themselves to have the required skills to protect themselves, see value in this protection and understand their place in the cyber-security chain. These findings emphasize the importance of understanding how individual differences relate to cyber-security behaviors in order to create more tailored human-centric interventions such as computer-based decision support systems and other human-machine interface solutions.