Reviewerly/exHarmony huggingface dataset:
Less is More: Proxy Datasets in NAS approaches

Neural Architecture Search (NAS) defines the design of Neural Networks as a search problem. Unfortunately, NAS is computationally intensive because of various possibilities depending on the number of elements in the design and the possible connections between them. In this work, we extensively analyze the role of the dataset size based on several sampling approaches for reducing the dataset size (unsupervised and supervised cases) as an agnostic approach to reduce search time. We compared these techniques with four common NAS approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of our surprising findings is that in most cases we can reduce the amount of training data to 25\%, consequently reducing search time to 25\%, while at the same time maintaining the same accuracy as if training on the full dataset. Additionally, some designs derived from subsets out-perform designs derived from the full dataset by up to 22 p.p. accuracy.
Introduction

The first author [9] developed a Gentzen-typed deduction system [14] in first-order logic, called R-calculus, to reduce a configuration $$\Delta |\Gamma $$ into a consistent theory $$\Delta \cup \Theta ,$$ where $$\Theta $$ is a minimal change of $$\Gamma $$ by $$\Delta ,$$ i.e., a maximal set of $$\Gamma $$ which is consistent with $$\Delta ,$$ where $$\Delta $$ is a set of atoms, and $$\Gamma ,\Theta $$ are consistent theories of first-order logic. Here, $$\Delta |\Gamma $$ corresponds to iterating revision $$\Gamma \circ \Delta .$$ Hence, the deduction system gives a concrete revision operator which is shown to satisfy AGM postulates.
Android App Antiforensics

Aim: Android is one of the most popular platforms in the market. This popularity has led the operating system to be a potential tool for criminal activities. Law enforcement has noted this development and started incorporating smartphone evidence into their cases. However, digital evidence is susceptible to data modification, and thus anti-forensic techniques have been developed to counter forensic investigations. This research investigates the possibility of generating false data using automation techniques. Methods: A rooted Android device was acquired. The device screen coordinates were mapped using screenshots and Gimp. The coordinates were used to develop a Python script to automate common user tasks such as making a phone call, sending a text message, or adding a contact. These tasks were performed manually and using the automation script. A system image was acquired of the device before and after data population. The images were analyzed using Autopsy and Cellebrite’s Inspector. The forensic artifacts retrieved were compared between the manual and automatic data population. Results: The artifacts show that the data was added successfully and that forensic tools may not detect that the data was automatically generated. Conclusion: This research shows that it is possible to populate an Android device with false forensic artifacts using automation scripts. Being able to generate forensic artifacts using automation scripts can allow educators to more easily generate datasets to teach forensic techniques. Additionally, it can also be used by malicious actors to generate false forensic artifacts to mislead an investigator. Future work could improve the proposed data generation technique via machine learning to prevent hardcoding the screen coordinates in the automation script or improve the technique to generate data with old timestamps. Another avenue of future work includes the development of techniques to identify false data.
Dynamics of miners' decision making under taxation in blockchain

Since participation in blockchain mining requires a lot of computational power and consumes a large amount of electricity, which leads to a significant level of carbon emissions, governments of some nations frame the policy about blockchain mining, such as cracking down on or imposing an additional tax for mining. We propose a dynamical model of miners' decision-making under the imposition of both an income and a green tax. We show that the mining reward affects the qualitative change of the behaviors of miners depending on the income tax rate and that both a saddle-node bifurcation and a transcritical bifurcation occur in the model.
Kernel Functions for Clustering of Incomplete Data: A Comparative Study

Clustering of incomplete dataset incorporating missing features is the most prevailing problem in the literature. Several imputations, as well as non-imputation techniques, are utilized to handle this problem. The kernel-based clustering strategies are discovered to be preferred in accuracy over the conventional ones. This paper aims to compare the performance of kernel-based clustering with different kernel functions when applied to incomplete data. These kernel functions have a significant role in kernel-based clustering. The selection of kernel function is neither simple nor insignificant. Gaussian kernel function is considered to be more valuable in different kinds of kernel-based clustering techniques explored in the literature. In this work, we have discussed Gaussian, non-Gaussian, and conditionally positive definite kernel functions for kernel-based clustering for handling missing data in clustering. This investigation is carried over on one artificial dataset and two different real datasets and presents an extensive study of kernel-based fuzzy c-means clustering using different kernel functions for clustering of incomplete data. Numerical analysis shows that other powerful kernel functions exist which work well in specific clustering applications.
Formal Verification for VRM Requirement Models

At the requirements level, formal verification and analysis are the focus of task’s attention which is developing complex systems by formal methods. Model checking is a technique for analysis and automated verification of complex safety-critical software systems. In this paper, a requirement model verification method based on formal technology is proposed to practice the model checking activity into the development process. Firstly, this essay analyzes syntax and semantics of models, which are defined by tabular expressions in VRM (variables relationship model). Then we preprocess the VRM model to classify into events tables, conditions tables and model class tables, and transform the VRM model into the automaton state transfer diagram with the help of semantic complementary work. Finally, we design an automatic model transformation framework from the VRM model to the model verification tool (nuXmv) and implement a translator between the formal specification language VRM and the symbolic model checker nuXmv. In this paper, we discuss our translation and abstraction approach in some depth and illustrate its feasibility with some preliminary examples.
Autonomous UAV Interception via Augmented Adversarial Inverse Reinforcement Learning

Intercepting enemy UAV in a low-altitude flight environment is a challenging task. Imitation learning provides a feasible solution for such challenge. Generative Adversarial Imitation Learning (GAIL) can learn expert strategies without a carefully designed reward function. In this paper, we propose a GAIL-based method to improve the autonomous interception of UAVs problem. In the method, we augment the expert data and introduce semantic condition into the Generative Adversarial Network (GAN) to improve learning efficiency. In addition, in the designed interactive environment, we use the proposed method to complete the UAV interception decision-making task and compare with two baseline methods by evaluating the interception performance through three performance metrics. The simulation results show that our method is better than all baseline methods on all of the three metrics.
A Framework Based Isolation Forest for Detecting Anomalies in Natural Gas Production

Natural gas is one of the main fossil fuels, and it is widely used in residential and industrial applications. The demand for natural gas is constantly increasing. However, due to the complex and diverse production environment for gas production, abnormal events that occur during the production of natural gas wells will reduce the gas production of gas wells with sufficient gas reservoirs. At present, detecting abnormal event in gas production mainly relies on engineers according to their own experience. This method is unreliable and requires a lot of manpower. In this paper, the first unsupervised framework for detecting anomalies in natural gas production is proposed. In this framework, a novel data convention method using a time window is proposed to enable the capture of the contextual anomaly. Besides, a low time-complexity and a small memory-requirement method called Isolation Forest is used to build a detector. Moreover, the maximum information coefficient (MIC) based feature selection mechanism reduces the high dimension caused by data convention in order to solve the increasing complexity of natural gas data sets. We apply our framework to several real natural gas well production data set labeled manually. Observations show that this framework increases the accuracy of the detection in the actual gas well production.
Dynamic Support Range based Rare Pattern Mining over Data Streams

Rare itemset mining is a relatively recent topic of study in data mining.In certain application domains, such as online banking transaction analysis, sensor data analysis, and stock market analysis, rare patterns are patterns with low support and high confidence that are extremely interesting when compared to frequent patterns.Numerous applications generate large amounts of continuous data streams.We require efficient algorithms capable of processing data streams in order to analyze them and find unique patterns.The strategies developed for static databases cannot be used to data streams.As a result, we require algorithms created expressly for data stream processing in order to extract critical unique patterns.Rare pattern mining is still in its infancy, with only a few ways available.To address this is developed the Dynamic Support Range-based Hybrid-Eclat Algorithm (DSRHEA), an Eclatbased technique for mining unique patterns from a data stream using bit-set vertical mining with two item-based optimizations.The detected patterns are kept in a prefix-based rare pattern tree that uses double hashing to maintain the unusual pattern in the data stream.Testing showed that the proposed method did well in terms of how long it took to run, how many rare patterns it made and accuracy.
The course of communicology in the system of vocational training of philologists

The purpose of this work is to share the experience of the Department of Ukrainian Kryvyi Rih State Pedagogical University in the field of communication studies, in particular, in organizing the practical work of students at bachelor’s degree, master’s Degree and PhD Degree. The activity of the teachers of our department is aimed at ensuring that students and postgraduate students acquire communicative competences, which allow the modern teacher to get to a high level of realization of professional functions based on national and world standards. The focus of this work is an analysis of compliance of the State standards in speciality “Philology” and the content of the programs of disciplines of the department, which ensure the quality of higher education. In this context, the author talks about the three stages of communicative education. We draw attention to our own practical experience in teaching the course in Ukrainian Communology for PhD students. As a result, postgraduate students learn to predict the results of communication in different situations, to notice mistakes in communication, to submit information to avoid conflicts correctly. They acquire the skills to make informed decisions and to carry out their professional activities in accordance with national communication standards. The article also discusses the prospects for communication education in Ukraine, which today has not yet become a mandatory humanitarian component of vocational training.
SWIS: Self-Supervised Representation Learning For Writer Independent Offline Signature Verification

Writer independent offline signature verification is one of the most challenging tasks in pattern recognition as there is often a scarcity of training data. To handle such data scarcity problem, in this paper, we propose a novel self-supervised learning (SSL) framework for writer independent offline signature verification. To our knowledge, this is the first attempt to utilize self-supervised setting for the signature verification task. The objective of self-supervised representation learning from the signature images is achieved by minimizing the cross-covariance between two random variables belonging to different feature directions and ensuring a positive cross-covariance between the random variables denoting the same feature direction. This ensures that the features are decorrelated linearly and the redundant information is discarded. Through experimental results on different data sets, we obtained encouraging results.
Generalized Net Model for Telecommunication Processes in Telecare Services

In a series of papers, Generalized Net (GN) models of processes, related to tracking changes in the health status of adult patients, have been presented. The contemporary state-ofthe- art of the telecommunications and navigation technologies allow these models to be further extended to the case of active and mobile patients. This requires the inclusion of patient’s current location as a new and significant variable of the model. Various opportunities are considered for the retrieval of this information, with a specific focus on the optimal ones, and a refined GN model is herewith proposed.
PENGARUH MODEL PEMBELAJARAN INQUIRY DALAM MENULIS TEKS PROSEDUR KOMPLEKS PADA SISWA KELAS XI

Indonesian language learning is to improve students’ ability to understand, analyze, evaluate, and produce. This is the background of the researcher to examine the effect of the inquiry learning model on the lesson of writing complex procedure texts. This study aims to improve students’ ability to write complex procedural texts using the inquiry learning model in class XI SMA Muhammadiyah 1 Medan for the 2020-2021 academic year. The population of this study is all students of class XI, totaling 103 students using a sample of 40 students with random sampling, the method used in this study is an experimental method with a two group pre-test and post-test design. The instrument used to test the ability to write complex procedure texts. The average value of ability in writing complex procedure texts before using the inquiry model is 72,1 while the average value of ability in writing complex procedure texts after using the inquiry model is 80,25. Based on the results of the pre-test and post-test class correlation analysis conducted with the product moment correlation formula, it obtained an r value of 0,965 so that thitung 22,456 &gt;ttabel 2,021, it was concluded that the inquiry model had a significant influence on the ability to write complex procedure texts in class XI high school students SMA Muhammadiyah 1 Medan Academic Year 2020-2021.
Low-Cost Nature-Inspired Deep Learning System for Pm2.5 Forecast Over Delhi, India

Air quality has a tremendous impact on India’s health and prosperity. Air quality models are crucial tools for surveying and projecting air pollution episodes, which can be used to issue health advisories to take action ahead of time. Short-term increases in air pollution trigger many adverse health events; a fast, efficient, cost-effective, and reliable air quality prediction model would aid in minimizing the effect on health and prosperity. Deterministic models, on the other hand, are less robust in predicting the pollutant series since it is non-stationary and non-linear. Atmospheric chemistry models are computationally expensive and often rely on outdated emissions information. We propose a deep learning model in this study that integrates neural networks, fuzzy inference systems, and wavelet transforms to predict the most prominent air pollutant affecting Delhi, India i.e., PM2.5 (particulate matter of aerodynamic diameter less than or equal to 2.5 microns). We have included the main aspects of air quality models in this research i.e., less computational time (7 minutes approximately using I5-1035G1, 1.19GHz processor), less resource-intensive (dependent only on the pollutant lagged values), and high spatial resolution (1km) for forecasting air quality three days ahead. The model predictions show a significant correlation coefficient lying in [0.96,0.98], [0.86,0.93], and [0.82,0.91] with Central Pollution Control Board (CPCB) monitored data at various sites in Delhi for one, two, and three days of forecast respectively.
MUSAL: Active Deep Learning Method Integrating Multiple Uncertainty Strategies for Classification

In order to obtain high efficiency of Deep Learning (DL) methods, it is necessary to train the model with a great amount of labeled data. However, in actual implementations, labeled data is always expensive. Active Learning (AL) is an effective method to obtain the valuable samples from massive unlabeled samples for labeling. Based on uncertain strategies, AL approaches have been extensively used due to its easiness of implementation. However, most of the current researches took only one of these strategies as the uncertainty criterion for sample selection, and few people considered strategies of integrating multiple uncertainties to improve the ability of model sample selection. In this work, an AL method integrated multiple uncertainty strategies (such as the Least Confidence, Margin Sampling, and Entropy, etc.)—MUSAL is proposed. It can overcome the shortcomings of underrepresented samples selected by a single criterion, and then efficiently obtain the most valuable samples for labeling. Compared with alternative learning models based on uncertainty methods on datasets such as the MNIST, Fashion-MNIST, SVHN, and CIFAR10, experimental results show that the proposed MUSAL can obtain advanced classification performance with decreased labeled samples.
A Research Overview and Cause Analysis of Parataxis and Hypotaxis

As different connecting means in Chinese and English, parataxis and hypotaxis have always been the focus of discussion and research by Chinese and foreign scholars. This pair of concepts has been studied for a long time, and after years of supplement and improvement, the concepts of parataxis and hypotaxis have been very mature and widely used in the translation practice of various styles. Therefore, the authors give an overview of the research on parataxis and hypotaxis from the aspects of the proposal, development and application of the concepts. Apart from that, the authors also analyze the reasons of parataxis and hypotaxis from the following two perspectives: (1) grammar and syntax, (2) culture and thinking modes. Through this study, the authors hope to further improve the accuracy and fluency of translation between Chinese and English based on theories of parataxis and hypotaxis, and also hope to find some reasonable explanations to the Chinese and Western cultural conflicts.
Firefighting with a Distance-Based Restriction

In the classic version of the game of firefighter, on the first turn a fire breaks out on a vertex in a graph $G$ and then $k$ firefighters protect $k$ vertices. On each subsequent turn, the fire spreads to the collective unburnt neighbourhood of all the burning vertices and the firefighters again protect $k$ vertices. Once a vertex has been burnt or protected it remains that way for the rest of the game. A common objective with respect to some infinite graph $G$ is to determine how many firefighters are necessary to stop the fire from spreading after a finite number of turns, commonly referred to as containing the fire. We introduce the concept of distance-restricted firefighting where the firefighters' movement is restricted so they can only move up to some fixed distance $d$ per turn rather than being able to move without restriction. We establish some general properties of this new game in contrast to properties of the original game, and we investigate specific cases of the distance-restricted game on the infinite square, strong, and hexagonal grids. We conjecture that two firefighters are insufficient on the square grid when $d = 2$, and we pose some questions about how many firefighters are required in general when $d = 1$.
Functional connectivity analysis on hierarchical reservoir computing model

The human brain performs intelligent behavior with its hierarchical neural structure. In the past years, the relationship among different brain areas and their roles have been widely studied. In particular, functional connectivity has been used to discuss mechanisms that perform information processing in the brain. Alternatively, computational models have also been used to reveal the mechanisms. Reservoir computing is one of the models that simulate the dynamical process of the brain. The purpose of this paper is to apply the functional connectivity analysis to the reservoir computing model. This approach will contribute to comparing the computational model with the neurophysiological study.
Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2

We present a new approach for universal texture synthesis by incorporating a multi-scale texton broadcasting module in the StyleGAN-2 framework. The texton broadcasting module introduces an inductive bias, enabling generation of broader range of textures, from those with regular structures to completely stochastic ones. To train and evaluate the proposed approach, we construct a comprehensive high-resolution dataset that captures the diversity of natural textures as well as stochastic variations within each perceptually uniform texture. Experimental results demonstrate that the proposed approach yields significantly better quality textures than the state of the art. The ultimate goal of this work is a comprehensive understanding of texture space.
Learning Music Through ICT

As discussed in detail in several chapters of this book, music as a cultural activity is closely linked to how it is produced (chapter “ From Individual Learning to Cooperative Learning ”). This is, in turn, mediated by the dominant technologies of each culture: from these technologies external representation systems are generated in which the actual music is coded, and from which it is produced (chapter “ Reading Music: The Use of Scores in Music Learning and Teaching ”).
Sequential Semantic Knowledge Graph Embedding

Knowledge graph embedding is aimed at representing entities and relations of knowledge graph in a low-dimensional continuous vector space. Previous embedding models pay little attention to the sequential semantic information in triples and as a result, may lead to the semantic drift problem. Towards this end, we propose a novel sequential semantic embedding (SeqSemE) model to address this problem in this paper. Firstly, we utilize a sequential language model to capture sequential information of triples and interactions between entities and relations. Secondly, we propose a method of learning two embeddings for each relation to avoid semantic drift. Extensive experiments on link prediction show that our SeqSemE is efficient and effective. It can obtain better performance than previous state-of-the-art embedding models.