Reviewerly/exHarmony huggingface dataset:
Modulation Recognition Method for Underwater Acoustic Communication Signal Based on Relation Network under Small Sample Set

Aiming at the poor performance of the modulation recognition method based on deep learning when there are only a few labeled samples in the target sea area, this paper proposes a modulation recognition method for underwater acoustic communication signal with small sample set based on relation network(RN). In this paper, the signal power spectrum is selected as the shallow feature representation of the signal based on the prior information of the signal and the cognition of the task-driven training mode of the relation network. And hence a modulation recognition model based on power spectrum and relation network is designed. This model is optimized by building small sample training tasks in different channels. This training mode improves the ability of the recognition method to quickly classify when only few labeled sample is available in the target sea area. Simulation experiments and the actual signal verify the advanced performance of our algorithm. Compared with existing recognition algorithms, our method requires fewer labeled samples and has better recognition effect.
“Average” Approximates “First Principal Component”? An Empirical Analysis on Representations from Neural Language Models

Contextualized representations based on neural language models have furthered the state of the art in various NLP tasks.Despite its great success, the nature of such representations remains a mystery.In this paper, we present an empirical property of these representations-"average" ≈ "first principal component".Specifically, experiments show that the average of these representations shares almost the same direction as the first principal component of the matrix whose columns are these representations.We believe this explains why the average representation is always a simple yet strong baseline.Our further examinations show that this property also holds in more challenging scenarios, for example, when the representations are from a model right after its random initialization.Therefore, we conjecture that this property is intrinsic to the distribution of representations and not necessarily related to the input structure.We realize that these representations empirically follow a normal distribution for each dimension, and by assuming this is true, we demonstrate that the empirical property can be in fact derived mathematically.
Problems of Protecting Virtual Game Property by Means of Criminal Law

The legal regulation of social relations in virtual reality is attracting an increasing attention of scholars. There are corresponding Russian and foreign publications concerning different branches of law: civil, tax, labor, international humanitarian, and criminology. Criminological research is based, among other things, on the analysis of sociocultural factors: how online gaming behavior influences the «cultural normalization» of similar behavior in the real world, the problems and boundaries of permitted violence. At the same time, the problem of infringements on virtual game property is becoming more urgent. The first reason for this is the fact that the possession of virtual property can have legal and economic consequences in the real world. The second reason is connected with the absence, in most jurisdictions, of the legal regulation of the emerging property rights (or other «rights in rem») to game property, which hinders prosecution for crimes against it, or makes it impossible in some countries where criminal law protection is linked to positive legislation. The authors believe that it is best to view these crimes as offences against property, and not cybercrimes in the narrow sense of the word. It is obvious that the problems of defining the crime of theft in the virtual space are connected with the understanding of the object and other features of theft, and the analysis thus mainly focuses on comparing the legal nature of virtual game property with how it corresponds to the features of theft in the current legislation. Key concepts of the legal nature of virtual game property are formulated, which are then analyzed from the standpoint of their applicability in criminal law. According to the authors, there are two main ways to solve the problem of virtual game property protection: either, following the approaches found in foreign practice, the definition of theft should be broadened, or the legal protection regime sui generis should be created to deal with the protection of all cyber aspects of property relations.
PENGARUH SELF EFFICACY TERHADAP PRESTASI BELAJAR MATEMATIKA SISWA SD DALAM PEMBELAJARAN DARING

Recently, education has been faced with learning that is not only carried out offline but also carried out online. Online learning has even been implemented since elementary education. This of course affects student learning outcomes. This study aims to determine the effect of self-efficacy on elementary students' mathematics learning achievement in online learning. This study used a quantitative survey method and was carried out on fourth grade elementary school students in Cluster II, Pleret Bantul District, consisting of 5 schools located in the sub-district area. The population in this study was 203 students and a sample of 132 students, with the technique used in sampling is proportional random sampling. Data collection techniques used are questionnaires and documentation. While the data analysis technique in this study used simple regression analysis with the help of SPSS 24.0. The results of the study concluded that there was a positive and significant influence between self-efficacy on elementary mathematics learning achievement in online learning. Data analysis resulted in a t-count value of 5.785 with a significance of 0.000, it was obtained t-table = 1.978 which can be seen from t-count &gt; t table = 5.785 &gt; 1.978 and a significance value of 0.000 &lt;0.05.
The Effect of Online Learning Process on Speaking Skill

This study aims to find out students' speaking skills during online learning. The research method used is quantitative research with ex-post facto research type. The research population was PGSD students second-semester which amounts to 316 people. The research sample was taken using a purposive sampling technique, with 118 students. Methods of data collection using a questionnaire and observation in an instrument. The data analysis technique used is descriptive analysis and inferential analysis. The results showed no effect of online learning on students' speaking skills. Students remain skilled at speaking even in online learning because it is influenced by the mindset of students so that they have the courage and high initiative to respond to the stimulus provided by the lecturer. The ability of lecturers to package impressive learning can also increase learning motivation so that students are more confident in speaking and expressing opinions.
Estimation of hourly global solar radiation using artificial neural network in Adana province, Turkey

Since global solar radiation (GSR) is an important parameter for the design, installation, and operation of solar energy-based systems, it is important to have precise information about it. As the indicating devices are expensive and their requirements such as operation and maintenance should be carried out, the measurement of solar radiation cannot be frequently taken. On the other hand, the measurements of different meteorological parameters such as relative humidity, sunshine duration, and ground surface temperature are more prevalent in meteorology stations. Therefore, the estimation of solar radiation is a significant parameter for the areas where the measurements could not be performed and to complete the missing information in databases. Many different models, software, and simulation programs are utilized to calculate solar radiation data, provide an economic advantage, and obtain high accuracy. The main purpose of this study is to perform an estimation of solar radiation in Adana, where is on the east of the Mediterranean in Turkey, by using an artificial neural network (ANN) model. The best estimation performance is obtained by optimizing the neuron numbers used in the network’s hidden layer with the trial and error method. With this aim, hourly data including wind speed, wind direction, humidity, sunshine duration, actual pressure, and average temperature are taken as inputs while solar radiation is taken as a target. All these data, which is for 2018, has taken from the Turkish State Meteorological Service. A linear correlation coefficient value has been obtained to be about 0.87313 with the mean square error (MSE) of 1.6184x104 W/m2 for the testing data set. The ANN’s testing/validation results show that it has a low MSE, indicating the accuracy and adequacy of the network model. Besides, the predicted ANN output is evaluated to be remarkably close to the measured target data by considering the linear correlation coefficient.
Pengaruh Penggunaan Aplikasi Zoom Meeting Terhadap Kemampuan Pemecahan Masalah Matematika Siswa Pada Pembelajaran Daring Di Kelas V

The purpose of this study is to explore the influence of the use of zoom meeting applications on students' math problem-solving skills in online learning in class V. The method in this study uses survey methods. The sample in this study was all VA SDN Duri Kepa 05 students as many as 30 students. Instruments to collect data used in this study are the use of zoom meeting applications and tests of students' math problem-solving skills. The results of the study obtained using a partial test (t) that showed that a significant 0.00 &lt; 0.05. These results showed H0 was rejected and H1 accepted, meaning there was a significant influence between the use of zoom on students' problem-solving abilities.
What drives Bitcoin fees? Using SegWit to assess Bitcoin’s long-run sustainability

Can Bitcoin remain tamper-proof in the long run? We use block-level data from the Bitcoin blockchain to estimate the impact of congestion and the US dollar price on fee rates. The introduction and adoption of the segregated witness (SegWit) protocol allows us to identify an aggregate demand curve for Bitcoin transactions. We find that SegWit has reduced fee revenue by about 70%. Fee revenue could be maximized at a block size of about 0.6 megabytes when SegWit adoption remains at current levels. At this block size, maximum fee revenue would be equivalent to one-eighth of the current average block reward. Hence, large sustained price increases are required to keep mining rewards constant in the long run.
Impacts of the Covid-19 Pandemic on Online Security Behavior within the UK Educational Industry.

The emergence of the Covid19 pandemic has caused the shutdown of university activities on campus in the UK. There has been a migration of learning, teaching and assessment (LTA) into online virtual environments. This in turn resulted in a spike in cyber-attacks across educational businesses, as they seemed more vulnerable due to inadequate security systems that would facilitate online learning. In this research, the impact of the corona virus pandemic on the security behavior of academic businesses is uncovered. This is done by comparing the pre-pandemic annual cyber security survey with the peak and post-pandemic survey, i.e., 2019, 2020 and 2021 respectively. Findings from this research demonstrate that the pandemic brought about a rise in cyber-attack, which made universities prioritize security, and add defensive measures to ease online learning. Overall, the post-pandemic statistics suggests that educational businesses have improved their systems with adequate security measures, which makes the migration to online learning safe. That said, cyber attackers are innovative and will continue to impose a threat to these institutions. Therefore, improving security systems is a continuous process even when the pandemic put under control significantly or even eradicated.
Data trust and data privacy in the COVID-19 period

Abstract In this article, we focus on data trust and data privacy, and how attitudes may be changing during the COVID-19 period. On balance, it appears that Australians are more trusting of organizations with regards to data privacy and less concerned about their own personal information and data than they were prior to the spread of COVID-19. The major determinant of this change in trust with regards to data was changes in general confidence in government institutions. Despite this improvement in trust with regards to data privacy, trust levels are still low.
Quantum Multi-factor Authentication

We present a quantum multi-factor authentication mechanism based on the hidden-matching quantum communication complexity problem. It offers step-up graded authentication for users via a quantum token. In this paper, we outline the protocol, demonstrate that it can be used in a largely classical setting, explain how it can be implemented in SASL, and discuss arising security features. We also offer a comparison between our mechanism and current state-of-the-art multi-factor authentication mechanisms.
SBIN: A stereo disparity estimation network using binary convolutions

Although the current advances on convolutional networks are outstanding, they mainly depend on extensive computational power, limiting the areas of applications. The latter applies for stereo disparity estimation, where current solutions can barely run on embedded devices. This work shows that it is possible to binarize an end-to-end stereo disparity network, which can be considered a step towards lightweight and potentially faster disparity estimation networks. This work shows the validity of the proposed approach through experimentation in two well-known datasets, sceneflow and kitti2012. The results show that a binary disparity model is possible but at the cost of performance. An EPE of 5.14 and 2.09 is achieved in sceneflow and kitti2012 accordingly.
Automatic Linking of Terms from Scientific Texts with Knowledge Base Entities

Due to the growth of the number of scientific publications, the tasks related to scientific article processing become more actual. Such texts have a special structure, lexical and semantic content that should be taken into account while processing. Using information from knowledge bases can significantly improve the quality of text processing systems. This paper is dedicated to the entity linking task for scientific articles in Russian, where we consider scientific terms as entities. During our work, we annotated a corpus with scientific texts, where each term was linked with an entity from a knowledge base. Also, we implemented an algorithm for entity linking and evaluated it on the corpus. The algorithm consists of two stages: candidate generation for an input term and ranking this set of candidates to choose the best match. We used string matching of an input term and an entity in a knowledge base to generate a set of candidates. To rank the candidates and choose the most relevant entity for a term, information about the number of links to other entities within the knowledge base and to other sites is used. We analyzed the obtained results and proposed possible ways to improve the quality of the algorithm, for example, using information about the context and a knowledge base structure. The annotated corpus is publicly available and can be useful for other researchers.
Evaluation of teaching quality based on binary tree support vector machine

In order to solve the reliability of the evaluation results of teaching quality in universities and colleges, an improved model of teaching evaluation based on the Support vector machine was put forward. In this model, the evaluator does not need to give an evaluation result of the teacher’s teaching quality, but gives the score of each evaluation index, and then calls the Support vector machine, automatic classification of teachers’ teaching quality. The experiment proves that the improved algorithm can improve the teaching quality evaluation accuracy and the result is better.
Navigation Fusion Algorithm Based on Improved A* Algorithm and Dynamic Window Approach

In the process of indoor mobile robot navigation, in order to improve the efficiency and safety of the planning algorithm, a global dynamic path planning of mobile robot is proposed by using the fifth-order Bessel curve optimization A* algorithm and integrating the dynamic window approach. Firstly, the A* algorithm is used to add the jump point search strategy to efficiently plan the global path in the grid map, and find out the break points in the path. Then, the fifth-order Bessel curve is used to generate a new local path with a certain curvature and a relatively short path between adjacent break points. Finally, the dynamic path planning is completed by combining the dynamic window approach. The experimental results show that the fusion algorithm has significantly improved the efficiency and path smoothness, and can dynamically avoid obstacles. The fusion algorithm has obvious excellent path planning ability.
Iterative Creation of Matching-Graphs – Finding Relevant Substructures in Graph Sets

Both the amount of data available and the rate at which it is acquired increases rapidly. The underlying data is often complex, making it difficult (or somehow unnatural) to represent it by vectorial data structures. Hence, graphs are a promising alternative for formalizing the data. Actually a large amount of graph-based methods for pattern recognition have been proposed. The vast amount of these methods rely on graph matching procedures. In a recent paper a novel encoding of graph matching information has been proposed. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs (called matching-graphs). In the present paper we aim to further improve the relevance of these matching-graphs by using an iterative creation algorithm. In an empirical evaluation we show that these novel matching-graphs offer a more stable and significant representation of their respective class than the previous version.
PENGEMBANGAN MEDIA PEMBELAJARAN E-LEARNING BERBASIS MOODLE PADA MATERI PECAHAN SENILAI KELAS IV SEKOLAH DASAR

This research and development produced a market-tested media and is suitable to be used by educators. This education media help students in their learning process whilst still having fun, especially in the topic of fractions. This research and development was built using the ASSURE model and adopted the evaluation process by Dick and Carey. The subjects of this RnD are the students from SDN Ragunan 01 Jakarta Selatan's 4th grade class, with a total of 18 students. Data collection was done through interviews and questionnaires and with Miles and Huberman data anlysis. Results of the research show that e-learning on Moodle is a learning media worth using, in this case for the purpose of teaching fractions to 4th graders. The results show an impressive expert review average score of 87.2% by the subjects in their understanding of the lesson. This media received positive response from the 3 selected students during the one to one tryout stage and a 96.42% mark by the subjects during the field trial stage. It is evident that this moodle-based e-learning media is properly made, well tested, and suitable for use in the education field - specifically for the purpose of teaching fractions to 4th graders.
Object Gathering With a Tethered Robot Duo

We devise a cooperative planning framework to generate optimal trajectories for a robot duo tethered by a flexible net to gather scattered objects spread in a large area. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we formulate an iterative optimization scheme to generate smooth and collision-free trajectories while ensuring cooperation within the robot duo to gather objects efficiently and avoid obstacles properly. We validate the generated trajectories in simulation and implement them in physical robots using Model Reference Adaptive Control (MRAC) to handle unknown dynamics of carried payloads. In a series of studies, we find that: (i) a U-shape cost function for maintaining separation distance is effective in planning cooperative robot duo, and (ii) the task efficiency is not always proportional to the tethered net’s length. Given an environment configuration, our framework can gauge the optimal net length. To our best knowledge, ours is the first that provides such estimation for tethered robot duo.
Object Detection and Classification Using Machine Learning Techniques

Object recognition and object detection are sub fields of computer vision, the task of giving computers the ability to perceive and respond to the world around them. This is a very useful technology and has many different applications in many different disciplines. Examples of applications include but are not limited to: Use in security by using facial recognition; use in the medical field for classification of cancer types (malignant or benign) or detecting sickle cells in the blood; or it can be used as a research tool to automatically record data (e.g. count the number of trucks that use a particular highway); or used in agriculture or industrialization for quality control; or for entertainment purposes in games that can detect and track a users' movement to control a character in a game. And there are many more examples, in which it can be used.
The Discrete Gaussian Expectation Maximization (Gradient) Algorithm for Differential Privacy

In this paper, we give a modified gradient EM algorithm; it can protect the privacy of sensitive data by adding discrete Gaussian mechanism noise. Specifically, it makes the high-dimensional data easier to process mainly by scaling, truncating, noise multiplication, and smoothing steps on the data. Since the variance of discrete Gaussian is smaller than that of the continuous Gaussian, the difference privacy of data can be guaranteed more effectively by adding the noise of the discrete Gaussian mechanism. Finally, the standard gradient EM algorithm, clipped algorithm, and our algorithm (DG-EM) are compared with the GMM model. The experiments show that our algorithm can effectively protect high-dimensional sensitive data.
Trash Stream: Social Conditioning of Criminalization

The social network as one of the digital technologies has not only creates a platform for communications, especially relevant during a pandemic, but also provokes the emergence of various types of deviant behavior, primarily due to the fact that many communicate on the Internet under fictitious names; it liberates a person, creates a feeling of impunity, control over the situation, etc. Recently, trash streams have become popular on the Web, but not funny and silly, but associated with violence, insult, humiliation of human dignity, causing a feeling of disgust and contrary to public morality. In December 2020, during such a live broadcast, another victim died, which launched a process in society to discuss the need to introduce criminal liability for such acts. The paper assesses the draft criminal law, as well as initiatives to supplement the list of aggravating circumstances and some corpus delicti with an appropriate qualifying feature, and formulates the author’s draft criminal law on responsibility for organizing, conducting, facilitating and participating in direct air in trash streams.