Reviewerly/exHarmony huggingface dataset:
The Difficulties Facing by Student in Using Participle in Sentences

This study deals with the student’s difficulties in using participle in sentences. The purposes of the study were to find out whether or not the students found difficulties in using participle in sentence and to find out the type of difficulties they faced. The population of the study was students. In this sampling, all the population has equal chance to be selected for the sample. The total numbers of samples was 32 students. The instrument used to collect the data was multiple choice test. This research was conducted by applying the descriptive quantitative design. The reliability of the test is counted by using KR21 formula. The formula testing result showed that the reliability of the test was 0,84, it means that the test was very good. The percentage of the student’s errors is dominantly occured in descriptive adjectives from 800 item occurrences, there were 436 errors which categorized into 5 sub categories on the use of adjectives and adverbs with the suffix–ly. The finding showed that the students found some difficulties in using participle, they were: Present Participle (22,52 %), Past Participle (63,73 %) and Perfect Participle (33,52 %). Past Participle was regarded as the most difficult type for them, especially in using it after certain verbs and in replacing relative pronoun, and then followed by Perfect Participle. And the last was Present Participle especially in using in it replacing relative pronoun and after certain verb. The percentage of each difficulty was taken by dividing the wrong answer to the total correct answer of the test.
Design and Simulation of a Neural Controller for MIMO Systems

Classical control theory is widely used to regulate Simple Input, Simple Output (SISO) systems. However, when there are multiple inputs and multiple outputs, the complexity to control the systems increases significantly. Intelligent control theories such as artificial neural networks have proven to be an efficient alternative to implement complex systems. In addition, it is not necessary to obtain a mathematical model of the system. This paper shows the application of artificial neural networks as control elements in MIMO systems (Multiple Input, Multiple Outputs). The performance of the controller is shown in a simulation of a combined system, in which two first-order plants are interconnected. The experiments were carried out in Matlab software.Velazquez-Gonzalez, Roberto S. Sosa-Savedra, Julio C. Barrera-Navarro, Agustin
Probing quantum nonlinearity of cavity-QED systems with quantum light

Giant optical circular birefringence (GCB) induced by a single quantum-dot-confined spin in an optical microcavity finds wide applications in quantum and optical technologies, such as quantum gates, quantum transistors, quantum repeaters, quantum routers, etc. If the system is probed with a classical light, such as the laser light where the photons are uncorrelated with each other, then the single-photon GCB is detected. In this work we develop a general approach to investigate the many-body dynamics of $n$ photons bound to one quantum emitter and apply this method to calculate the $n$-photon GCB probed with quantum light in Fock states. With suppressed atomic saturation, quantum light loads photons into dressed states more efficiently than classical light such that the whole Jaynes-Cummings energy ladder and the quantum nonlinearity related to the multiphoton transitions can be observed. The $n$-photon GCB lying at the cavity mode resonance allows the spin-cavity quantum gates and quantum transistors to extend from single-photon to $n$-photon operations and generate entangled photonic Fock states, i.e., the NOON states which are useful for quantum metrology and lithography.
ACT: Anonymous Consensus Based on Tor

As the core of blockchain technology, consensus mechanism determines the security, stability and availability of the system. Most of consensus mechanisms including Po-X, X-BFT and so on mainly focuse on the design and improvement of the consensus layer while the privacy of nodes is ignored. It leads to the emergence of node corruption and conspiracy attacks. In this paper, we present ACT, anonymous consensus mechanism for consortium blockchain. ACT uses anonymous communication mechanism, based on Onion Routing Technology Tor, to protect node privacy and prevent the corruption of consensus nodes. To reduce the computational power consumption, ACT abandons the traditional attempt conversion and mining mechanism while double ring threshold signature is adopted. Besides, we design a set of identity authentication mechanism NKC for ACT based on the improved Keberos protocol, which reduces the probability of Byzantine nodes to a certain extent.
Shared Vulnerability: Transparency as Facilitator During Pandemic Learning

This chapter explores the intentional use of transparency while teaching an online Master of Social Work (MSW) course during the beginning of the COVID-19 pandemic. First the author recognizes how the unfolding crisis necessitated altered boundaries between herself and her students. Then the author presents four ways she used transparency to help students stay engaged and involved in the course, including changing expectations, expressing feelings, prioritizing safety and well-being, and requesting help. Finally, the author reflects upon how these Spring 2020 adjustments will inform her future pedagogy.
Learning to Augment Imbalanced Data for Re-ranking Models

The conventional solution to learning to rank problems ranks individual documents by prediction scores greedily. Recent emerged re-ranking models, which take as input initial lists, aim to capture document interdependencies and directly generate the optimal ordered lists. Typically, a re-ranking model is learned from a set of labeled data, which can achieve favorable performance on average. However, it can be suboptimal for individual queries because the available training data is usually highly imbalanced. This problem is challenging due to the absence of informative data for some queries and furthermore, the lack of a good data augmentation policy.
Problems of image recognition systems associated with the possibility of its use in terrorist attacks

Abstract This article discusses systems for recognizing human faces, as well as systems for identifying flying objects. The paper deals with the main security issues related to the recognition of faces and images of objects. Today, automation systems that help to recognize an object, compare it with existing databases, can help prevent terrorist attacks, unauthorized penetrations, as well as the proliferation of biological and nuclear weapons. To date, the study of issues related to the possibility of using these systems is relevant and very much in demand.
Security and Complexity of a New Variant of the McEliece Cryptosystem Using Non-linear Convolutional Codes

The McEliece public-key cryptography (PKC) has fewer encryption/decryption operations compared to other PKC schemes such as RSA, ECC, and ElGamal. The use of Goppa codes in its implementation ensures the hardness of the decoding problem. Conversely, the original McEliece PKC has a low encryption rate and large key size. In this paper, a new variant of the McEliece cryptosystem is presented based on non-linear convolutional codes. Cascaded convolutional codes are used to be part of the public key with each stage of the cascade separated by a product cipher to increase the security level. Convolutional codes are used as an alternative to Goppa codes since the Viterbi decoding algorithm is suitable for high data-rate applications by providing maximum-likelihood solutions. The convolutional code used in the implementation increases both security and throughput due to its high error-correcting capacity. It is shown that the new variant has small key sizes with enhanced security-complexity trade-off. Cryptanalysis of the new version of the McEliece cryptosystem is performed using existing attacks of the classical cryptosystem to demonstrate the difficulties in breaking the new cryptosystem. Also, it is shown that security levels comparable to the original McEliece cryptosystem could be obtained by using smaller public key sizes of the new version if multiple stages of the generator matrix are employed. This aspect makes the new version of the McEliece cryptosystem attractive in mobile wireless networks since it could be ported onto a single Field Programmable Gate Array (FPGA).
NoPeak: An Intelligent Multi-hop Scheduling Scheme for Optical Data Center

The ever-increasing bandwidth requirements of modern datacenters have led researchers to employ optical circuit switching (OCS) to reduce the cost of data center networks, but most of these proposals these schemes assume that the traffic in the network is uniform, which will lead to the traffic cannot be fully invoked in the real environment. We propose an intelligent multi-hop scheduling algorithm capable of supporting any one hop scheduling algorithm to reduce the impact of traffic imbalance on the overall performance of the network. Through the intelligent multi-hop method, the algorithm can transport the oversubscribed traffic with the minimum overall link occupancy. Simulation results show that the proposed algorithm can reduce the average end-to-end delay by 46.4% and the average stream completion time by 51%, and the throughput is improved by 20.1%.
Dynamic gesture recognition based on CNN-LSTM-Attention

Compared with traditional human-computer interaction techniques, gesture recognition is closer to human expression habits and have some advantages of being efficient and easy to master. Vision-based gesture recognition does not require additional equipment, and is very convenient and relatively low cost. To recognize dynamic gesture in complex background, we build a backbone network based on SSD with dilated convolution, which greatly improves the quality of the detected feature maps, and then we proposes a CNN-LSTM-Attention based dynamic gesture recognition network. The spatial features of dynamic gestures at each moment are first extracted from gesture sequences, then these features are transformed into dynamic gesture spatio-temporal features by a recurrent neural network with an attention mechanism, and finally fed into a fully connected neural network for gesture recognition. The dynamic gesture recognition network achieves 93.5% recognition rate on Sahand dataset, which exhibits its effectiveness.
Identification of the Writer of Historical Documents via Geometric Modeling of the Handwriting

In this work, a generic framework is developed that simultaneously embeds 2D shapes’ registration, comparison and grouping, via the assumption that shapes of the same class are distorted level-sets of the same implicit function. The corresponding system is developed so as to deal with the automatic classification of documents according to their writers. The data that the system processes are the realizations of the individual characters, which are mutually aligned per document and per character, modulo affine transformations, and then reduced to a single representative shape. Stationarity conditions of these representatives are then used to statistically test the hypothesis that different documents bear a single representative. The considered documents are grouped according to their writer, by determining the maximal groups that also maximize the joint probability of the classification, computed over the characters, which occur in all documents. We have tested the system on 26 pages of Byzantine manuscripts that preserve Iliad. The computed classification of these pages in 4 writers has been independently verified by expert scientists.
Memory Efficient Hardware Architecture for 5/3 Lifting-Based 2-D Forward Discrete Wavelet Transform

Novel memory efficient hardware architecture for 5/3 lifting-based two-dimensional (2-D) forward discrete wavelet transform (DWT) is presented in this paper. The proposed novel architecture requires total memory of size less than 4N in case of J levels of decomposition for an N×N image, which is the lowest size of required total memory compared with the best of the existing state-of-the-art solutions. This novel architecture is able to process multiple levels of decomposition simultaneously by using only one one-dimensional (1-D) 5/3 lifting-based horizontal filter and one 1-D 5/3 lifting-based vertical filter. Since 5/3 DWT filter is the default filter for reversible transformation in JPEG 2000 standard, the proposed hardware architecture is quite suitable for the implementation in a JPEG 2000 coder.
A Rank-Based Sampling Framework For Offline Reinforcement Learning

Offline reinforcement learning (RL) is an attractive method that learns a policy purely from a previously collected dataset without additional interaction. However, it suffers the data quality issue that the performance of the policy is largely dependent on the previously collected dataset. In this paper, we propose a novel and robust sampling technique, Rank-Based sampling (RBS) to address this issue for offline RL. This method samples the transition with high return more frequently and reduces the influence of outliers. In this way, each transition has attached a rank of importance for non-uniform sampling. RBS approximates the optimal value of each state in offline dataset through the upper envelope neural network. Our method is compatible with many existing offline RL methods, e.g. Batch-Constrained deep Q-learning (BCQ), a hyperparameter-robust offline RL algorithm. In this paper, we provide a comprehensive experimental study in simulation environments. BCQ with RBS achieves a new state-of-the-art of performance, outperforming vanilla BCQ with uniform sampling and two other algorithms.
Adversarial Robustness for Latent Models: Revisiting the Robust-Standard Accuracies Tradeoff

Low-dimensional structure of data can solve the adversarial robustness-accuracy conflict for machine learning systems. Modern machine learning systems have demonstrated breakthrough performance in a multitude of applications. However, they are known to be highly vulnerable to small perturbations to the input data, known as adversarial attacks. There are many well-documented examples of such behavior, for example small perturbations of an image, which is imperceptible to a human, can significantly degrade performance of modern classifiers. Adversarial training has been put forward as a way to improve robustness of learning algorithms to adversarial attacks. However, this benefit often comes at the cost of decreasing accuracy on natural unperturbed inputs, pointing to a potential conflict between adversarial robustness and standard accuracy. In “Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff,” Adel Javanmard and Mohammad Mehrabi develop a theory to show that when the data enjoys low-dimensional structure, then it is possible to train models that are nearly optimal with respect to both, the standard and robust accuracies.
Digital Currency System and the Effectiveness of Blockchain Verification in Computer Information System Environment

The paper will develop a new digital currency system to greatly improve the node message transmission function and the effectiveness of blockchain verification. The paper proposes a random key pre-distribution scheme based on hash chain. The article effectively adjusts the length of the hash chain, the number of public auxiliary nodes, and the number of hash chains. Nodes only need to pre-distribute a small amount of key information to establish a digital currency micropayment quality audit duality with a higher probability.
DRBANET: A Lightweight Dual-Resolution Network for Semantic Segmentation with Boundary Auxiliary

Due to the powerful ability to encode image details and semantics, many lightweight dual-resolution networks have been proposed in recent years. However, most of them ignore the benefit of boundary information. This paper introduces a lightweight dual-resolution network, called DRBANet, aiming to refine semantic segmentation results with the aid of boundary information. DRBANet also adopts dual parallel architecture, including: high resolution branch (HRB) and low resolution branch (LRB). Specifically, HRB mainly consists of a set of Efficient Inverted Bottleneck Modules (EIBMs), which learn feature representations with larger receptive fields. LRB is composed of a series of EIBMs and an Extremely Lightweight Pyramid Pooling Module (ELPPM), where ELPPM is utilized to capture multi-scale context through hierarchical residual connections. Finally, a boundary supervision head is designed to capture object boundaries in HRB. Extensive experiments on Cityscapes and CamVid datasets demonstrate that our method achieves promising trade-off between segmentation accuracy and running efficiency.
Smart Food Fare Canteen: Automation of Bills and Serving

Nowadays, technology is upgrading and advancing in multi-folded ways and dimensions. The real time application considered is the canteen bill system. In the traditional system, the user will take a static bill for the kind of food that may be needed to feed on the bill counter. This leads to many users would experience odd kind of issues such as few may waste the food because that is not tasty, few users may not consume more food because of their potentiality, and few may take more than they deserve. In such cases, in order to set right, the situation that prefers a token for the trail to check up the food is tasty with a certain 10% of fare of that specific food. Manual billing is avoided, and automatic billing is accounted for using debit cards or QR codes through authorized payment gateway apps. Each item in the canteen is assigned a certain price and a scanner and QR code for it; the user has to choose which items to eat after the trail of food items is over. This way avoids wastage of bills that protects trees indirectly and also saves the environment. This automated billing and efficient catering of quality food and its items would be the demanding in the business in the future. In this way, two sections are there in which one is trial session, and the other is real food court session. Both are separated sessions but connected.
Pedagogical conditions for forming the project competence of future teachers in the university environment

The relevance of the problem under study is associated with an important task of the higher education system the preparation of students future teachers for project activities in an educational institution. A modern teacher should have a wide range of knowledge, skills and abilities in the field of project activities. The purpose of the paper is a theoretical substantiation of pedagogical conditions that contribute to the formation of project competence of future teachers in the university environment. As designated pedagogical conditions are named and described: organization of training in cooperation; introduction of an additional course Learning to work on a project into the educational process; the use of the project method as a form of independent work of students in the study of various disciplines of the curriculum. The scientific novelty of the research lies in the fact that pedagogical conditions have been developed and substantiated, contributing to the effective formation of the project competence of future teachers in the conditions of a university. The theoretical significance of the study lies in the fact that its results enrich the theory and methodology of vocational education in the field of forming the project competence of future teachers; complement scientific ideas about the pedagogical conditions for the effective formation of project competence, as well as about the features of its functioning; scientific approaches are systematized. The practical significance of the study lies in the fact that the revealed pedagogical conditions lead to the effective formation of the project competence of future teachers in the conditions of a university. The developed additional course can be applied in the practice of teaching universities in order to improve the quality of professional training of future teachers. The experimental base of the study was the budgetary institution of higher education Surgut State University. The methods used in the research: generalization, analysis and synthesis of information, comparison, analysis of educational and project activities of students. The effectiveness of preparing students - future teachers - for project activities with students is an urgent task in the education system, which can be ensured through the implementation of certain pedagogical conditions. It is the pedagogical conditions that increase the effectiveness of educational and cognitive activities and contribute to the most fruitful work of the teacher and students.
The Negative Phase of the Transitional Age

Marx, Andy Blunden argues (2017), wrote Capital twice: first he worked through the factual material to work out his basic concepts in the Grundrisse, and then he returned and re-established all the factual material by working it through his basic concept to produce his magnum opus. Vygotsky does this too, as we saw in Chapter 5 on the newborn and elsewhere. But he inverts the usual procedure for this lecture, the last in our book. Previous lectures almost always began with factual material of one kind or another, worked through them with an eye to establishing the social situation of development and the main line of development, and ended with a clear description of the central neoformation of the age, how it resolved the social situation, and what it implied for the upcoming stage. This was even true of critical ages like one, three, and seven.
Learning Spatio-temporal Representation by Channel Aliasing Video Perception

In this paper, we propose a novel pretext task namely Channel Aliasing Video Perception (CAVP) for self-supervised video representation learning. The main idea of our approach is to generate channel aliasing videos, which carry different motion cues simultaneously by assembling distinct channels from different videos. With the generated channel aliasing videos, we propose to recognize the number of different motion flows within a channel aliasing video for perception of discriminative motion cues. As a plug-and-play method, the proposed pretext task can be integrated into a co-training framework with other self-supervised learning methods to further improve the performance. Experimental results on publicly available action recognition benchmarks verify the effectiveness of our method for spatio-temporal representation learning.
Ontology for Traditional Modernized Dance Jaipong Oray Welang

Indonesia has many ethnic groups and each ethnic group has various types of dances. Jaipong Oray Welang is one of the most popular modernized traditional dances originating from the Sundanese people. In an effort to preserve this dance, it is necessary to record the knowledge, not only in terms of descriptions and other attributes but also recording a series of dance moves along with pictures of examples of each dance movement. In this study, the ontology of the Jaipong Oray Welang dance has been built. Dance movement data was obtained from observations and interviews of dancers in a Sundanese dance studio. Ontology is implemented using the protégé application and owlready2 in python, and competency questions are evaluated using SPARQL queries.