Reviewerly/exHarmony huggingface dataset:
Estimating the Relationship Between Block Size and Block Propagation Time in Bitcoin by Simulation

Bitcoin is known to have poor transaction processing performance. A performance improvement is expected by increasing block size, but how much improvement can be achieved in theory is not well understood. In this paper, by using a blockchain simulator called Simblock, we numerically evaluate the effect of the block size on block propagation time. Then, based on the evaluation results, we discuss the possibility of improving transaction processing performance. As a result, it is estimated that the expected value of transaction processing capacity can be improved to about 2,592 transactions per second by approaching the block propagation time to the block generation time interval.
Stabilization of Impulse Processes of the Cognitive Map of Cryptocurrency Usage with Multirate Sampling and Coordination Between Some Nodes Parameters

In this paper, based on the cognitive map (CM) of the cryptocurrency usage in financial markets developed in Romanenko et al. [1], models of impulse processes with multirate sampling are applied to describe the dynamics of fast and slow measurable coordinates of the CM nodes in the format of systems of difference equations (Roberts equations [2]). Control subsystems with multirate sampling for fast and slow measurable nodes coordinates to stabilize them at the given levels under the effect of random disturbances were developed. External disturbances include various informational influences that affect the cryptocurrency application system. Mutual influences of related subsystems of CM impulse processes are classified as internal disturbances. External control vectors for closed-loop subsystems with fast and slow measurable coordinates of the CM nodes are implemented by varying some coordinates that can be changed by the decision-maker in closed-loop control systems. Additionally, in the closed-loop control subsystem with fast measured coordinates, the subsystem of dynamic coordination of a given ratio of the coordinates of the CM nodes describing the volume of supply and demand for cryptocurrency on financial exchanges is implemented. The designed discrete controllers were studied using digital simulation.
A Fuzzy-Based System for Selection of Radio Access Technologies in 5G Wireless Networks: Effect of Security Parameter on Quality of Experience Assessment

The 5-th Generation (5G) heterogeneous networks are expected to provide various applications and wide area networks to fulfill user requirements. They consist of many types of Radio Access Technologies (RATs) and the selection of a RAT should consider many parameters, which makes the selection decision an NP-hard problem. One of critical parameter for selecting a RAT in 5G wireless networks is Quality of Experience (QoE). In order to deal with this problem, in this paper we propose a Fuzzy-based system for assessment of QoE considering four parameters: Network Capacity (NC), Experienced End-User Throughput (EEUT), Connectivity (Cn) and Security (Se), which is considered as a new parameter. From simulation results, we conclude that the highest value of QoE is achieved when the RAT provides a big NS, fast EEUT, high Cn and very strong Se.
Automatic Generation of Handwritten Style Characters Including Untrained Characters

In this paper, we propose a method to generate handwritten style characters by extracting the characteristics of a person’s handwritten characters. We use DCGAN (Deep Convolutional Generative Adversarial Networks) to generate handwritten style characters, which preserves the unique characteristics of a person’s handwritten characters. Some methods to generate handwritten style characters have been proposed so far. However, there are too many Kanji characters to prepare handwritten characters as training data; therefore, it is impossible to generate handwritten style characters by extracting the characteristics of a person’s handwritten characters including Kanji characters. In facts, since there are 2,136 Kanji characters for regular use, these would require a large amount of training time and training effort. Our method generates characters that have never been written before by decomposing and extracting Kanji characters into elements, learns each element separately, generates each element, and merges them. Therefore, the system implemented by the method can learn from a limited number of handwritten characters and automatically generate characters that do not exist in the training data, and can generate characters that preserve the characteristics of a person’s handwritten characters. Thus, the number of handwritten style characters that can be generated automatically can be significantly increased.
Research on Cross-Domain Authentication Scheme for V2G Networks Based on SM9 Signature Cryptography Algorithm and Consortium Blockchain Technology

With the rapid development of electric vehicles worldwide, the vehicle to grid (V2G) network, which is an important component of the smart grid, is facing a huge security threat. Especially, the convergence effect generated when a large number of devices connect to the V2G network can easily cause server authentication congestion, forming a signaling data storm, leading to a sharp increase in authentication latency. To solve this problem, this paper fully analyzes the advantages of the consortium blockchain technology and the V2G Multidomain Network architecture, and proposes a cross domain security management authentication scheme for V2G network based on SM9 digital signature algorithm and the consortium blockchain technology. This scheme can achieve secure and reliable two-way authentication between local domain users and local domain servers, as well as between local domain users and foreign domain servers, and can also achieve safe and effective storage and sharing of information flow and current to improve the security of energy transaction data, transparency of information, and level of automation certification in a distributed network environment. This will actively promote the popularization of electric vehicles.
Survey of Security Proof of Public-Key Scheme

In the field of modern cryptography, public key cryptography is widely used in various application scenarios in cyberspace. At present, a large number of public key schemes have been presented, but security proof of which has always been a difficulty. Furthermore, there is no corresponding work to introduce the idea of security proof of it currently. We usually only grasp several concept of security proof of public key schemes, but is difficult to clarify the whole logic of it. This paper overview the proof idea of public key schemes, and helps us understand why we need to introduce security model, how to prove the security of public key scheme through contradiction, how to generate a simulated scheme from an example of the hard problem based on which the real scheme is designed, and the difficulties of security proof. Finally, through specific examples, we can better understand the proof idea of public key schemes, and help us design more safer scheme in the field of public key cryptography.
Artifact Extraction Methods for In-Vehicle Infotainment System

Digital forensics was primarily focused on analyzing mobile devices and hard drives. However, with the advent of smart cars, there is a growing interest in forensic analysis of vehicles. Smart cars are equipped with computer-like functions, allowing them to store and process information. One of the most important components for collecting user data in smart cars is the In-Vehicle-Infotainment (IVI) system. Despite ongoing research efforts, the forensic analysis of the IVI system remains insufficient and requires further exploration. To research an IVI system’s artifacts, you need a way to enter the IVI system. In this study, we researched common entry points into IVI systems and how to extract artifacts from them. We used various infotainment systems from different manufacturers for this study. Therefore, we can conclude that IVI systems have common entry points, and these extraction methods can be used across different systems.
Analisis Penggunaan Fukushi “Taihen” pada buku Minna No Nihongo

Adverbs (fukushi) are adverbs, not subjected to deformation. Fukushi taihen belongs to the type of Teido no Fukushi, which is an adverb that serves to express a level of state and activity. In addition to taihen, other fukushi that fall into the type of teido no fukushi include totemo, hijooni, osoroshiku, hidoku, daibu, zuibun, amarini, kanari, kekkoo, nakanaka, sukoshi, chotto, zutto, motto, masumasu. The word taihen that appears in textbooks both Minna no Nihongo 1 and 2 and Kaiwa Hyougen no Sakuin used in Kaiwa Enshuu courses is almost entirely the word taihen as an adjective. The purpose of this study was to describe the meaning of the word taihen in Japanese sentences both as adjectives and adverbs. The results of the study are as follows: (1) As an adjective, the word taihen in the sentences that are data in this study has various meanings even though at first glance it looks the same. These meanings are exhausting, difficult, troublesome, troublesome, and impractical (until it ends up being troublesome). It is from this fact that it is found that the word taihen in textbooks is more negative in tone, because of the various meanings that have been analyzed, none of them have positive nuances. (2) As an adverb (fukushi) that expresses a degree of circumstance, in general the word taihen translates to "very" into Indonesian. Based on the results of the analysis that has been presented in the previous chapter, the meaning of the word taihen as an adverb is the expression of something seriously with deep or striking feelings and something unusual or unexpected. Unlike taihen as an adjective that is all negative in tone, taihen as an adverb has two nuances at once both positive and negative, depending on what word it describes.
Feature Selection Based on a Decision Tree Genetic Algorithm

The feature selection problem has become a key undertaking within machine learning. For classification problems, it is known to reduce the computational complexity of parameter estimation, but also it adds an important contribution to the explainability aspects of the results. In this paper, a genetic algorithm for feature selection is proposed. The importance, as well as the effectiveness of features selected by each individual, is evaluated by using decision trees. The feature importance indicated by the decision tree is used during selection and recombination. The tree inducted by the best individual in the population is used for classification. Numerical experiments illustrate the behavior of the approach.
The 3D photogrammetry approach of tiny model using the rotative scanning

Purpose This paper aims to discuss the scanning methodology depending on the close-range photogrammetry technique, which is appropriate for the precise three-dimensional (3D) modelling of objects in millimetres, such as the dimensions and structures in sub-millimetre scale. Design/methodology/approach The camera was adjusted to be tilted around the horizontal axis, while coded dot targets were used to calibrate the digital camera. The experiment was repeated with different rotation angles (5°, 10°, 15°, 20°, 25°, 30°, 50° and 60°). The images were processed with the PhotoModeler software to create the 3D model of the sample and estimate its dimensions. The features of the sample were measured using high-resolution transmission electron microscopy, which has been considered as a reference and the comparative dimensions. Findings The results from the current study concluded that changing the rotation angle does not significantly affect the results, unless the angle of imagery is large which prevent achieving about 20: 30% overlap between the images but, the more angle decreases, the more number of images increase as well as the processing duration in the programme. Originality/value Develop an automatic appropriate for the precise 3D modelling of objects in millimetres, such as the dimensions and structures in sub-millimetre scale using photogrammetry.
Open Tools for Data Literacy Development at School. A Case of Systemic and Experience Design Approach to Civic Tech Education

As technology literacy is recognized as a crucial 21st-century skill to confront the challenges of the rapidly approaching digital transformation, a significant concern within formal educational settings revolves around how to effectively train young students. The objective is to move beyond the confines of traditional computer science teaching methods that solely rely on keyboards and mice. Instead, the focus should be on fostering explorative and creative activities that emphasize the collection and utilization of data. This paper presents the methodological framework of a one-year pilot project. Through a co-design process, the project aimed to address the pressing need for a more comprehensive and innovative approach to developing data literacy in schools. The project achieved this by designing open connected tools, educational resources, and facilitating a collaborative process based on active citizenship.
Reinforcing Assessment Processes Using Proactive Case-Based Reasoning Mechanisms

The integration of case-based reasoning mechanisms into E-learning systems is not a recent option. These mechanisms allow us to implement reasoning systems using similar experiences in a retrospectively way, which is quite attractive for problem solving in some application areas. Its application promotes the achievement of very interesting solutions, especially when an interesting case base exists a priori, integrating knowledge and appropriate problem-solving methods. In this paper, we present and describe a hybrid reasoning system especially developed to support student knowledge assessment processes in an E-learning platform. The system integrates two autonomous reasoning modules, which combine their functioning during knowledge assessment processes, combining rule-based and case-based knowledge, respecting a set of assessment restrictions defined a priori by a human supervisor.
Exploratory Study of Data Sampling Methods for Imbalanced Legal Text Classification

This article investigates the application of machine learning algorithms in the legal domain, focusing on text classification tasks and addressing the challenges posed by imbalanced class distributions. Given the very high number of ongoing legal cases in Brazil, the integration of machine learning tools in the workflow of courts has the potential to enhance justice efficiency and speed. However, the imbalanced nature of legal datasets presents a significant hurdle for traditional machine learning algorithms, which tend to prioritize the majority class and disregard minority classes. To mitigate this problem, researchers have developed imbalance learning techniques that either modify supervised learning or improve the dataset class distribution to improve predictive performance. Data sampling techniques, such as oversampling and undersampling, play a crucial role in balancing class distributions and enabling the training of accurate machine learning models. In this study, a real dataset comprising lawsuits from the Court of Justice of São Paulo, in the state of São Paulo, Brazil, is used to evaluate the effects of different imbalance learning techniques, including oversampling, undersampling, and combined methods, in predictive performance for a binary classification task. The experimental results provided valuable insights into the comparative performance of these techniques and their applicability in the legal domain.
Towards an Induction Principle for Nested Data Types

A well-known problem in the theory of dependent types is how to handle so-called nested data types. These data types are difficult to program and to reason about in total dependently typed languages such as Agda and Coq. In particular, it is not easy to derive a canonical induction principle for such types. Working towards a solution to this problem, we introduce dependently typed folds for nested data types. Using the nested data type $$\texttt {Bush}$$ as a guiding example, we show how to derive its dependently typed fold and induction principle. We also discuss the relationship between dependently typed folds and the more traditional higher-order folds.
Comprehensive Analysis of Different Techniques for Data Augmentation and Proposal of New Variants of BOSME and GAN

In many environments in which detection of minority class instances is critical, the available data intended for training Machine Learning models is poorly distributed. The data imbalance usually produces deterioration of the trained model by generalisation of instances belonging to minority class predicting as majority class instances. To avoid these, different techniques have been adopted in the literature and expand the original database such as Generative Adversarial Networks (GANs) or Bayesian network-based over-sampling method (BOSME). Starting from these two methods, in this work we propose three new variants of data augmentation to address data imbalance issue. We use traffic event data from three different areas of California divided in two subgroups attending their severity. Experiments show that top performance cases where reached after using our variants. The importance of data augmentation techniques as preprocessing tool has been proved as well, as a consequence of performance drop of systems in which original databases with imbalanced data where used.
Investigation of Artificial Intelligence Methods in the Short-Term and Middle-Term Forecasting in Financial Sphere

In this paper the problems of short- and middle-term forecasting at the financial sphere are considered. For this problem intelligent forecasting methods: GMDH and hybrid deep learning networks based on self-organization are suggested. As alternative method was used ARIMA The optimal parameters of hybrid networks and ARIMA were found. Optimal structures of hybrid networks were constructed for short-term and middle-term forecasting. The experimental investigations of GMDH and hybrid DL networks in forecasting problems in finance are carried out and compared with ARIMA thereafter the best forecasting spheres for GMDH and hybrid DL networks were determined.
Design of Single Radio Frequency Fingerprint Identification Algorithm for Aviation Equipment

Radio frequency fingerprint (RFF) identification technology works in the physical layer and has unique advantages in the field of aviation equipment identification. This paper focuses on the RFF identification algorithm in aviation equipment. The specific work is as follows. Firstly, we analyzed the recent research on RFF identification in aviation equipment. Secondly, we developed a novel RFF identification model based on wavelet transform for the measured data, which included modules such as data analysis/processing, establishment of characteristic parameter standard database, classification and optimization. Finally, the recognition rate of the model was analyzed. The results show that, when the samples are sufficient, the average recognition rate of aviation equipment is 55.65% and when the samples are limited, the average recognition rate of aviation equipment is 45.03%.
Model Performance Prediction: A Meta-Learning Approach for Concept Drift Detection

Data stream applications in highly dynamic environments often face concept drift problems, a phenomenon in which the statistical properties of the variables change over time, which can degrade the performance of Machine Learning models. This work presents a new model monitoring tool through the use of Meta Learning. The algorithm was conceived for data streams with concept drift and large target arrival delay. Additionally, a new set of Meta Features is proposed based on the use of unsupervised concept drift metrics. Unlike related Meta Learning approaches, a regressor was used at the meta level to predict the predictive performance of the base model. These predictions can be used to generate concept drift alerts before the arriving objects are labelled. Experimental results show that the proposed approach obtains, on average, a classification error reduction of 12.8%, when compared to the traditional Meta Learning approaches, and 38%, when compared to the baseline, the last known performance, in predicting the performance of the base model.
Parallelism in Realizability Models

Study of parallel operations such as Plotkin’s parallel-or has promoted the development of the theory of programming languages. In this paper, we consider parallel operations in the framework of categorical realizability. Given a partial combinatory algebra A equipped with an “abstract truth value” $$\varSigma $$ (called predominance), we introduce the notions of $$\varSigma $$ -or and $$\varSigma $$ -and combinators in A. By choosing a suitable A and $$\varSigma $$ , a form of parallel-or may be expressed as a $$\varSigma $$ -or combinator. We then investigate the relationship between these combinators and the realizability model $$\textbf{Ass}(A)$$ (the category of assemblies over A) and show the following: under a natural assumption on $$\varSigma $$ , (i) A admits $$\varSigma $$ -and combinator iff for any assembly $$X\in \textbf{Ass}(A)$$ the $$\varSigma $$ -subsets (canonical subassemblies) of X form a poset with respect to inclusion. (ii) A admits both $$\varSigma $$ -and and $$\varSigma $$ -or combinators iff for any $$X\in \textbf{Ass}(A)$$ the $$\varSigma $$ -subsets of X form a lattice with respect to intersection and union.
Engaging Learners in Familiarizing Themselves with Sensors and Actuators

During the last years, more researchers and educators have proposed workshops to familiarize learners with concepts based on smart city and smart object design. The designing process is demanding, as it requires exploring sensors and actuators which behave as activators of smart city design, but also ideate, program, and prototype new solutions to enhance smart city features. While workshops usually are based on educational boards and block-based programming environments, this paper reports on the design of in-person workshops, which are aimed at engaging high-school learners across the entire design process of smart city solutions while exploiting professional technologies and components like that of STMicroelectronics. An engagement questionnaire was administered online to understand whether participants considered the proposed workshops enjoyable and easy to follow. While learners had fun, STMicroelectronics components are perceived as slightly difficult to learn.
Monte-Carlo Tree Search for Multi-agent Pathfinding: Preliminary Results

In this work we study a well-known and challenging problem of Multi-agent Pathfinding, when a set of agents is confined to a graph, each agent is assigned a unique start and goal vertices and the task is to find a set of collision-free paths (one for each agent) such that each agent reaches its respective goal. We investigate how to utilize Monte-Carlo Tree Search (MCTS) to solve the problem. Although MCTS was shown to demonstrate superior performance in a wide range of problems like playing antagonistic games (e.g. Go, Chess etc.), discovering faster matrix multiplication algorithms etc., its application to the problem at hand was not well studied before. To this end we introduce an original variant of MCTS, tailored to multi-agent pathfinding. The crux of our approach is how the reward, that guides MCTS, is computed. Specifically, we use individual paths to assist the agents with the goal-reaching behavior, while leaving them freedom to get off the track if it is needed to avoid collisions. We also use a dedicated decomposition technique to reduce the branching factor of the tree search procedure. Empirically we show that the suggested method outperforms the baseline planning algorithm that invokes heuristic search, e.g. A*, at each re-planning step.