Reviewerly/exHarmony huggingface dataset:
The Use of Social Media for the Development of Digital Literacy of Students: From Adequate Use to Cognition Tools

The study focuses on the possibility of turning social media into an effective tool to improve digital literacy and facilitate cognition. In view of this, a one-month experiment involving 268 eighth graders from two Moscow schools was con-ducted; the participants were divided into a control and an experimental group. The students were encouraged to independently search and develop social net-work skills to solve educational issues and improve cognition. Objective perfor-mance tests were conducted based on the curriculum results. According to the ac-ademic performance, the mean grade on a 12-point scale in the experimental group increased from 7.77 to 8.76 points, while in the control group the change remained within the statistical error (7.64 and 7.76, respectively). A strong corre-lation between the grades obtained in different groups (0.8 when comparing the final results in two groups) was found. The study can be put into practice within the framework of the widespread introduction of social media in school curricula as a tool to access information, develop digital literacy and the ability to use digi-tal cognition tools being supervised and supported by a skillful teacher.
Kind inference for datatypes

In recent years, languages like Haskell have seen a dramatic surge of new features that significantly extends the expressive power of their type systems. With these features, the challenge of kind inference for datatype declarations has presented itself and become a worthy research problem on its own. This paper studies kind inference for datatypes. Inspired by previous research on type-inference, we offer declarative specifications for what datatype declarations should be accepted, both for Haskell98 and for a more advanced system we call PolyKinds, based on the extensions in modern Haskell, including a limited form of dependent types. We believe these formulations to be novel and without precedent, even for Haskell98. These specifications are complemented with implementable algorithmic versions. We study soundness, completeness and the existence of principal kinds in these systems, proving the properties where they hold. This work can serve as a guide both to language designers who wish to formalize their datatype declarations and also to implementors keen to have principled inference of principal types. This technical supplement to Kind Inference for Datatypes serves to expand upon the text in the main paper. It contains detailed typing rules, proofs, and connections to the Glasgow Haskell Compiler (GHC).
An Interdisciplinary Model for Graphical Representation

The paper questions whether data-driven and problem-driven models are sufficient for a software to automatically represent a meaningful graphical representation of scientific findings. The paper presents descriptive and prescriptive case studies to understand the benefits and the shortcomings of existing models that aim to provide graphical representations of data-sets. First, the paper considers data-sets coming from the field of software metrics and shows that existing models can provide the expected outcomes for descriptive scientific studies. Second, the paper presents data-sets coming from the field of human mobility and sustainable development, and shows that a more comprehensive model is needed in the case of prescriptive scientific fields requiring interdisciplinary research. Finally, an interdisciplinary problem-driven model is proposed to guide the software users, and specifically scientists, to produce meaningful graphical representation of research findings. The proposal is indeed based not only on a data-driven and/or problem-driven model but also on the different knowledge domains and scientific aims of the experts, who can provide the information needed for a higher-order structure of the data, supporting the graphical representation output.
Key Legal Issues Surrounding Smart Contract Applications

The aim of this work is to delve into some key legal issues surrounding Smart Contract applications. In the first and second parts, this paper defines the concept of Smart Contract and how it applies within the blockchain ecosystem. In the third part, this work exposes the functioning of this digital technology and the main advantage that it implies for solving the crucial issues of execution and breach of contracts. In the fourth section, this paper analyzes a set of problematic aspects that arise around Smart Contracts such as the scope and delimitation of them or the validity and certification of blockchain transactions. It also analyzes different issues related to the design of a legislative policy that addresses the design of a supranational and harmonized legal framework.
Solving isomorphism problems about 2‐designs from disjoint difference families

Abstract Recently, two new constructions of disjoint difference families in Galois rings were presented by Davis, Huczynska, and Mullen and Momihara. Both were motivated by a well‐known construction of difference families from cyclotomy in finite fields by Wilson. It is obvious that the difference families in the Galois ring and the difference families in the finite field are not equivalent. A related question, which is in general harder to answer, is whether the associated designs are isomorphic or not. In our case, this problem was raised by Davis, Huczynska, and Mullen. In this paper, we show that, in most cases, the 2‐ designs arising from the difference families in Galois rings and those arising from the difference families in finite fields are nonisomorphic by comparing their block intersection numbers.
Quantum tomography with random diagonal unitary maps and statistical bounds on information generation using random matrix theory

We study quantum tomography from a continuous measurement record obtained by measuring expectation values of a set of Hermitian operators obtained from unitary evolution of an initial observable. For this purpose, we consider the application of a random unitary, diagonal in a fixed basis at each time step and quantify the information gain in tomography using Fisher information of the measurement record and the Shannon entropy associated with the eigenvalues of covariance matrix of the estimation. Surprisingly, very high fidelity of reconstruction is obtained using random unitaries diagonal in a fixed basis even though the measurement record is not informationally complete. We then compare this with the information generated and fidelities obtained by application of a different Haar random unitary at each time step. We give an upper bound on the maximal information that can be obtained in tomography and show that a covariance matrix taken from the Wishart-Laguerre ensemble of random matrices and the associated Marchenko-Pastur distribution saturates this bound. We find that physically, this corresponds to an application of a different Haar random unitary at each time step. We show that repeated application of random diagonal unitaries gives a covariance matrix in tomographic estimation that corresponds to a new ensemble of random matrices. We analytically and numerically estimate eigenvalues of this ensemble and show the information gain to be bounded from below by the Porter-Thomas distribution.
Penguatan Literasi Alam Kampung Ilmu Purwakarta

The government's biggest homework now is how to improve remote areas, especially education issues. However, some groups have initiatives in building their own literacy centers in remote areas. It is a "Village of Science" in Cisarua Village, Tegalwaru District, Purwakarta Regency, West Java. located between Mount Bongkok and Mount Parang. The distance from the city center of Purwakarta is a 2 hour drive through the Jatiluhur toll gate in Purbaleunyi. This village mostly has junior high school graduates, but in the hands of a husband and wife couple, Imam B. Prasodjo and Gita Prasodjo the area is now one of the centers of literacy civilization in Purwakarta. Through the World Conscience Foundation, Together with the team of the Indonesian Writers Institute, Chairman Bambang Trim, the author was assigned to contribute to SDN students in Purwakarta to develop students in literacy, as well as to develop the ability of teachers to assist students in literacy. The activities carried out are providing assistance in terms of reading and writing, both for teachers and students. In this activity, it was seen that the enthusiasm of students and teachers was very high in improving their literacy skills.&#x0D;
Colorectal Cancer Image Segmentation and Classification with Deep Neural Network Based on Information Theory

Colorectal cancer (CRC) is the development of cancer from the colon or rectum. Microsatellite instability (MSI) status can be considered as an indicator to predict the prognosis of CRC. We employ MSI prediction of CRC image by designing a neural network model of which base network is DeepLabv3+ with OctaveResNet. Additionally, we add a channel sort module to divide a feature map along with channel intensity. Then each feature map goes through distinct convolution paths. Each convolution path is designed based on information theory: the most important feature goes through the lightest convolution path, vice versa. By dividing feature map and applying different amount of convolutional operation, the model can extract features efficiently. In the experiment, total model weight is reduced but accuracy increases.
Influence Distribution Training Data on Performance Supervised Machine Learning Algorithms

Almost all fields of life need Banknote. Even particular fields of life require banknotes in large quantities such as banks, transportation companies, and casinos. Therefore Banknotes are an essential component in carrying out all activities every day, especially those related to finance. Through technological advancements such as scanners and copy machine, it can provide the opportunity for anyone to commit a crime. The crime is like a counterfeit banknote. Many people still find it difficult to distinguish between a genuine banknote and counterfeit Banknote, that is because counterfeit Banknote produced have a high degree of resemblance to the genuine Banknote. Based on that background, authors want to do a classification process to distinguish between genuine Banknote and counterfeit Banknote. The classification process use methods Supervised Learning and compares the level of accuracy based on the distribution of training data. The methods of supervised Learning used are Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Naïve Bayes. K-NN method is a method that has the highest specificity, sensitivity, and accuracy of the three methods used by the authors both in the training data of 30%, 50%, and 80%. Where in the training data 30% and 50% value specificity: 0.99, sensitivity: 1.00, accuracy: 0.99. While the 80% training data value specificity: 1.00, sensitivity: 1.00, accuracy: 1.00. This means that the distribution of training data influences the performance of the Supervised Machine Learning algorithm. In the KNN method, the greater the training data, the better the accuracy.
Detecting Vulnerabilities of Web Application Using Penetration Testing and Prevent Using Threat Modeling

The number of Web attacks is increasing gradually, mainly the popularity of Web application in organization, school, and colleges. For this reason, the security of their sensitive information against attacker becomes very important for all organization and companies. In this paper, we describe different type of Web application attack like SQL injection, XSS attack, CSRF attack, and Buffer overflow. Besides, we discuss about different types of penetration tools for Web applications. Penetration testing try to find the vulnerabilities of Web application so that we can build a defense mechanism to deal with Web attack. Finally, we build attack trees and defense trees to represent the attacks and to prevent those attack.
Activity Identification from Natural Images Using Deep CNN

With the increasing demand for surveillance, robotics, health-care, sports play, human-computer interaction and human activity classification & recognition have become a contemporary research topic in the computer vision era. Classification of human activity from video dataset is a really challenging task in the field of computer vision due to high dimensionality, various activities, the variability of inter-class and intraclass, background behavior, actor movement and multiple signs of interaction. The convolutional neural network (CNN) in deep learning plays an active role in today’s technology for classification and recognition patterns from sequence images in the video which is superior to the conventional machine learning methods. In this paper, we propose two convolutional neural network-based deep learning models that are suitable for human activity recognition. Initially, we have applied some optimization techniques in this model that eliminate the major problem of data overfitting. The results of the proposed models show that the proposed optimization methods are increasing the CNN performance higher than the conventional two CNN models. The models trained and tested were KTH and UCF11 YouTube action dataset which contains a large amount of different human activity videos with different conditions. The proposed two models were modified from the two traditional neural network models and the results show that the output of the proposed models achieved the classification accuracy greater than the traditional convolutional neural network models.
Observability of the relative motion from inertial data in kinematic chains

Real-time motion tracking of kinematic chains is a key prerequisite in the control of, e.g., robotic actuators and autonomous vehicles and also has numerous biomechanical applications. In recent years, it has been shown that, by placing inertial sensors on segments that are connected by rotational joints, the motion of that kinematic chain can be tracked accurately. These methods specifically avoid using magnetometer measurements, which are known to be unreliable since the magnetic field at the different sensor locations is typically different. They rely on the assumption that the motion of the kinematic chain is sufficiently rich to assure observability of the relative pose. However, a formal investigation of this crucial requirement has not yet been presented, and no specific conditions for observability have so far been given. In this work, we present an observability analysis and show that the relative pose of the body segments is indeed observable under a very mild condition on the motion. We support our results by simulation studies, in which we employ a state estimator that neither uses magnetometer measurements nor additional sensors and does not impose assumptions on the accelerometer to measure only the direction of gravity, nor on the range of motion or degrees of freedom of the joints. We investigate the effect of the amount of excitation and of stationary periods in the data on the accuracy of the estimates. We then use experimental data from two mechanical joints as well as from a human gait experiment to validate the observability criterion in practice and to show that small excitation levels are sufficient for obtaining accurate estimates even in the presence of time periods during which the motion is not observable.
Distance education — modern realities and development prospects

Researched is the problem of distance education in Russia in the context of the coronavirus pandemic that spread throughout the world in 2020. It describes and analyzes results of survey in the form of questionnaire of both Students and Lectors of Higher Educational institutions in several provincial cities of Russia. As a result of the study it was revealed that about 70% of students and more than 90% of Lectors believe, that the educational process in higher education is inappropriate to completely transfer online. Lectors noted that optimal form of organizing the educational process is combination of online and offline formats. The survey in the form of questionnaire made it possible to assess strengths and weaknesses of organization and content ofdistance education in Russian Universities, difficulties and problems that have arisen among Lectors and Students, and prospects for development of the phenomenon under study. First of all, it is necessary to note insufficient readiness of both Lectors and Students to switch to distance mode. Many organizational difficulties were associated with unstable operation of electronic educational environment, educational portals, information and communication networks, as well as with failures in the Internet. Research has shown that these issues need to be addressed over the long term. Both Students and Lectors recognize that distance learning is a good addition to existing traditional educational system, and some forms of educational work can be left online.
FamilyGAN: Generating Kin Face Images Using Generative Adversarial Networks

Automatic kinship verification using face images involves analyzing features and computing similarities between two input images to establish kin-relationship. It has gained significant interest from the research community and several approaches including deep learning architectures are proposed. One of the law enforcement applications of kinship analysis involves predicting the kin image given an input image. In other words, the question posed here is: “given an input image, can we generate a kin-image?” This paper attempts to generate kin-images using Generative Adversarial Learning for multiple kin-relations. The proposed FamilyGAN model incorporates three information, kin-gender, kinship loss, and reconstruction loss, in a GAN model to generate kin images. FamilyGAN is the first model capable of generating kin-images for multiple relations such as parent-child and siblings from a single model. On the WVU Kinship Video database, the proposed model shows very promising results for generating kin images. Experimental results show 71.34% kinship verification accuracy using the images generated via FamilyGAN.
A Multimodal Tensor-Based Late Fusion Approach for Satellite Image Search in Sentinel 2 Images

Earth Observation (EO) Big Data Collections are acquired at large volumes and variety, due to their high heterogeneous nature. The multimodal character of EO Big Data requires effective combination of multiple modalities for similarity search. We propose a late fusion mechanism of multiple rankings to combine the results from several uni-modal searches in Sentinel 2 image collections. We fist create a K-order tensor from the results of separate searches by visual features, concepts, spatial and temporal information. Visual concepts and features are based on a vector representation from Deep Convolutional Neural Networks. 2D-surfaces of the K-order tensor initially provide candidate retrieved results per ranking position and are merged to obtain the final list of retrieved results. Satellite image patches are used as queries in order to retrieve the most relevant image patches in Sentinel 2 images. Quantitative and qualitative results show that the proposed method outperforms search by a single modality and other late fusion methods.
Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs

Distributed data processing systems like MapReduce, Spark, and Flink are popular tools for analysis of large datasets with cluster resources. Yet, users often overprovision resources for their data processing jobs, while the resource usage of these jobs also typically fluctuates considerably. Therefore, multiple jobs usually get scheduled onto the same shared resources to increase the resource utilization and throughput of clusters. However, job runtimes and the utilization of shared resources can vary significantly depending on the specific combinations of co-located jobs. This paper presents Hugo, a cluster scheduler that continuously learns how efficiently jobs share resources, considering metrics for the resource utilization and interference among co-located jobs. The scheduler combines offline grouping of jobs with online reinforcement learning to provide a scheduling mechanism that efficiently generalizes from specific monitored job combinations yet also adapts to changes in workloads. Our evaluation of a prototype shows that the approach can reduce the runtimes of exemplary Spark jobs on a YARN cluster by up to 12.5%, while resource utilization is increased and waiting times can be bounded.
On Computational Hardness of Multidimensional Subtraction Games

We study the algorithmic complexity of solving subtraction games in a fixed dimension with a finite difference set. We prove that there exists a game in this class such that solving the game is EXP-complete and requires time 2Ω(n), where n is the input size. This bound is optimal up to a polynomial speed-up. The results are based on a construction introduced by Larsson and Wästlund. It relates subtraction games and cellular automata.
Underwater image enhancement method based on the generative adversarial network

Aiming at the problems of color distortion, nonuniform illumination, and low contrast caused by degradation of underwater images, an underwater image enhancement method (MSFF-GAN) based on generative adversarial network was proposed. A multiscale featured fusion generator is designed, which improves the ability to use different scale features of the model and ensures that the generated image retains more detailed information. The residual dense module is constructed to solve the problem of generator characteristics extracted slower. In the discriminator, to achieve the extraction of local image features, the output matrix is discriminating so that the generated image is closer to the real image. Compared with the existing underwater image enhancement methods qualitatively and quantitatively, the proposed method has better enhancement effect on EUVP and RUIE datasets. The proposed method is superior to the contrast method of three evaluation indexes: PSNR, SSIM, and UIQM.
Word frequency–rank relationship in tagged texts

We analyze the frequency-rank relationship in sub-vocabularies corresponding to three different grammatical classes (nouns, verbs, and others) in a collection of literary works in English, whose words have been automatically tagged according to their grammatical role. Comparing with a null hypothesis which assumes that words belonging to each class are uniformly distributed across the frequency-ranked vocabulary of the whole work, we disclose statistically significant differences between the three classes. This results point to the fact that frequency-rank relationships may reflect linguistic features associated with grammatical function.