Reviewerly/exHarmony huggingface dataset:
Mediated semi-quantum key distribution in randomization-based environment

This paper proposes the first mediated semi-quantum key distribution in randomization-based environment with an untrusted third party(TP) who has the complete quantum capabilities to help two classical users to establish a secure key. The entanglement swapping between the collapsed qubits of Bell states and the Bell states is used to facilitate the design of the RSQKD protocol.
A Adaptabilidade de NPCs em Jogos com Estilo Presa-Predador Usando Algoritmo Genético

Recently there has been a major effort to improve intelligence andadaptability in games to satisfy modern gamers. In this sense, thegames area has focused on the application of artificial intelligencealgorithms, especially genetic algorithms. This paper shows anapproching using a genetic algorithm to improve the adaptationof non-player characters (NPCs) in a prey-predator style game,presenting good results.
Toward Few-step Adversarial Training from a Frequency Perspective

We investigate adversarial-sample generation methods from a frequency domain perspective and extend standard $l_\infty $ Projected Gradient Descent (PGD) to the frequency domain. The resulting method, which we call Spectral Projected Gradient Descent (SPGD), has better success rate compared to PGD during early steps of the method. Adversarially training models using SPGD achieves greater adversarial accuracy compared to PGD when holding the number of attack steps constant. The use of SPGD can, therefore, reduce the overhead of adversarial training when utilizing adversarial generation with a smaller number of steps. However, we also prove that SPGD is equivalent to a variant of the PGD ordinarily used for the l∞ threat model. This PGD variant omits the sign function which is ordinarily applied to the gradient. SPGD can, therefore, be performed without explicitly transforming into the frequency domain. Finally, we visualize the perturbations SPGD generates and find they use both high and low-frequency components, which suggests that removing either high-frequency components or low-frequency components is not an effective defense.
An experimental study on the production of Korean affricates by Chinese learners of Korean

The purpose of this study is to analyze pronunciation errors and acoustic characteristics on Korean affricates produced by Chinese learners of Korean. To do so, listening judgement tests by Korean natives and acoustic analysis were conducted against produced tokens by Chinese learners. Listening judgement results showed that Chinese learners did not articulate lenis accurately in the ‘zha’ and ‘zhi’ groups and had difficulties in pronouncing lenis with being confused with fortis sounds in word-initial. They did show some confusions with lenis and fortis sounds reciprocally in the ‘azha’ and ‘azhi’ groups in word-medial position. On the aspect of pronunciation improvement, produced tokens of the ‘zha’ group showed an improvement to some degree with the accumulation of learning experience but those of the ‘zhi’ group rarely showed the effect of learning experience. In word-medial, produced tokens of the ‘azha’ group showed some improvement with the accumulation of learning experience. The ‘azhi’ group, however, was not affected by learning experience at all. According to the results of acoustic analysis Chinese learners of Korean distinguished types of affricates in the ‘zha’ group by CF-VOT. For the ‘zhi’ group, CF-VOT was the main factor to distinguish aspirated from lenis and fortis sounds while distinguishing lenis from aspirated using f0 (Chinese females). In word-medial, The Chinese learners’ production cues for the affricates in ‘azha’ and ‘azhi’ groups were almost the same as those of the Korean group. One more thing to mention is that Chinese males distinguished lenis from fortis and aspirated sounds by means of f0.
LibGuides: Criminal Justice: Citations and Writing Help

This guide is designed to assist students in finding resources for their Criminal Justice classes.
Ternary coded melody as blind audio watermark

In this paper, we developed a new technique for blind embedding of ternary coded watermarks into audio files. Usage of ternary coding increases payload of the method that can be considered as an advantage against binary-coded watermarks. A well-known melody is presented as a sequence of ternary digits (trits) and is used as a watermark. This sequence is embedded into the time domain of a host audio file through amplitude modulation and B-splines. There is a version of that procedure where the clean copy of the container is necessary for extraction watermark [1]. In our approach, we exclude that container and convert the method into a blind one. The strong correlation between neighbor samples in the container is used to this end. A procedure based on neuron net is suggested for enhancement perception of ternary coded music. In this case, we exploit the correlation between samples in the watermark melody. It is supposed that a person checks the mark's existence, and he/she can recognize the melody even after significant distortions. The resistance of the technique to the most successful attacks is investigated. The paper is an extended version of the conference paper [1].
A Survey on Feature Selection Based Spam Review Detection using Deep Learning Techniques

In recent times, online shoppers are technically knowledgeable and open to product reviews. They usually read the buyer reviews and ratings before purchasing any product from ecommerce website. For the better understanding of products or services, reviews provided by the customers gives the vital source of information. In order to buy the right products for the individuals and to make the business decisions for the Organization online reviews are very important. These reviews or opinions in turn, allow us to find out the strength and weakness of the products. Spam reviews are written in order to falsely promote or demote a few target products or services. Also, detecting the spam reviews has also become more critical issue for the customer to make good decision during the purchase of the product. A major problem in identifying the fake review detection is high dimensionality of the feature space. Therefore, feature selection is an essential step in the fake review detection to reduce dimensionality of the feature space and to improve the classification accuracy. Hence it is important to detect the spam reviews but the major issues in spam review detection are the high dimensionality of feature space which contains redundant, noisy and irrelevant features. To resolve this, Deep Learning Techniques for selecting features is necessary. To classify the features, classifiers such as Naive Bayes, K Nearest Neighbor are used. An analysis of the various techniques employed to identify false and genuine reviews has been surveyed.
Predictive Analysis and Data Visualization Approach for Decision Processes in Marketing Strategies: A Case of Study

In this paper, we perform a new strategy for recommender systems in online entertainment platforms. As a case of study, we analyzed the reading preferences based on users of Goodreads, a social network for readers, to classify the books depending on their associated with variables as average rating, rating count, and text review count. Multivariate techniques cluster analysis and benchmarking for comparison of predictive models were used. Graphs and data are presented, allowing optimal evaluation of the number of clusters and the precision of models. Finally, we show the existence of groups of elements that can be forgotten by traditional recommendation systems, due to their low visualization on the platform. It is proposed to use promotional strategies to highlight these high-quality articles but with little visibility. All in all, consider the classification of books that predictive models can offer, it can favor the authors, readers, and investors of Goodreads, by the retention and attraction of users.
Fabulous Stories in Preschool Ecological Education

The paper describes results of an experimental research aimed to identify pedagogical conditions of using fairy tales in senior preschool children’s ecological education. The authors establish requirements for choosing ecological tales, reveal techniques of working with an ecological tale in senior preschool classes, identify stages of senior preschool children’s ecological education on the basis of fairy tales. Scientific originality of the study involves identifying pedagogical conditions of using fabulous stories in senior preschool children’s ecological education. The research findings are as follows: the authors justify efficiency of using fabulous stories in senior preschool children’s ecological education.
Development of Aerator for Water Saving

Global technology keepson improving continually from time to time especially in product design and development. In this work, a proposed spray aerator design was implemented in an effort to adopt an initiative related to the water savingpolicy. This paper presents an experimental method of evaluation to develop the aerator used in ablution (wudhuâ€™ in Islamic practice) to improve the water saving activity. Results showed that the proposed aeratordesign improved the water efficiencyof the flowrate by 88% compared tothe earlier design and deemed to have more potentials to obtain higher economic benefits. The improveddesign managed to reducethe water flowrate and consumption at a desirable level. The recorded data provided information on the duration and volume of the water consumption per use and it was deemed helpful in evaluating the human behaviour during ablution. The study also highlights the fabrication of the aerator using polymeric materials with the aid if a 3D printing process. The results were acquired from the simulation and later analysed using theANSYS FLUENT Version 19.2 software. Finally, the optimum conditions for promoting the effectiveness in term of saving is to support the water management activities were established, taking into account the beneficial role of water consumption.
Exploring the Role of Argument Structure in Online Debate Persuasion

Online debate forums provide users a platform to express their opinions on controversial topics while being exposed to opinions from diverse set of viewpoints. Existing work in Natural Language Processing (NLP) has shown that linguistic features extracted from the debate text and features encoding the characteristics of the audience are both critical in persuasion studies. In this paper, we aim to further investigate the role of discourse structure of the arguments from online debates in their persuasiveness. In particular, we use the factor graph model to obtain features for the argument structure of debates from an online debating platform and incorporate these features to an LSTM-based model to predict the debater that makes the most convincing arguments. We find that incorporating argument structure features play an essential role in achieving the better predictive performance in assessing the persuasiveness of the arguments in online debates.
Reinforcement Learning Method with Generalization Ability Developed by Using Deep Learning for Solving a Path Finding Problem

Nowadays, deep learning and reinforcement learning have given high performance in various fields, and attracted much attention. In order to apply these learning methods to real problems, they must have a sufficient generalization ability. Whereas to improve the generalization ability has been actively studied in some fields such as image recognition and speech recognition, it has not been sufficiently studied for sequential decision-making problems such as game play and path finding. This paper proposes a reinforcement learning method with the generalization ability developed by using deep learning for a path finding problem, which is one of the sequential decision-making problems. Experimental results show that the generalization ability of the proposed method is superior to that of deep learning methods and deep reinforcement learning methods.
PENERAPAN PENDEKATAN ARSITEKTUR PERILAKU DALAM MERANCANG PERPUSTAKAAN UMUM GUNA MENDUKUNG KEGIATAN LITERASI DI KOTA DENPASAR

The decline in statistics on public visits to the library draws attention to review. Changing patterns and lifestyles of people shift people's views about libraries. The fast-paced era of digital literacy has shifted people's interest in libraries. Increasing time and era, libraries are no longer used as a lifestyle by the community. Human behavior, which is implemented as a form of architecture, is also known to shape human behavior. The buildings that were originally designed and shaped by humans as their fulfillment will then assist the human way in living their social life and values ​​in life. The old paradigm of society towards the library is an old building that is dark, stuffy, lonely, and even haunted. Therefore it is necessary to have a study of what kind of design can captivate the hearts of today's people so that they can then apply it in an architectural design by studying the patterns and lifestyles of today's society with the aim of obtaining a library design concept that is in accordance with the lifestyle of the community (especially students) today and the reactivation of library building facilities which are increasingly disappearing from the pattern of people's lives.
Analisando Tweets Relacionados a Deficiências: uma Abordagem Baseada em Classificação

Approximately 80 % of people with some form of physical, mental,or intellectual disability live in developing countries. These samecountries have shown significant growth in the availability of theinternet. Such facts reveal good possibilities regarding access toemotional support and experiences exchange among people withdisabilities through social media. However, hate speech and derogatorycomments about these people can be a recurring problem onthese platforms. In order to identify these posts, this article featuresa classifier developed using Twitter posts related to disabilities.The results show that the tool developed is promising in detectingoffensive and pejorative comments on this topic, which can be usedin content management systems.
ЭФФЕКТИВНОСТЬ ПРОГРАММ ISPRING ПРИ ДИСТАНЦИОННОМ ОБУЧЕНИИ(EFFICIENCY OF ISPRING PROGRAMS IN DISTANCE LEARNING)

The need to improve distance learning technologies in the modern education system is urgent. Although e-textbooks and various educational resources have been developed for many secondary schools, such resources in higher education institutions are insufficient. For active communication and effective feedback between students and teachers, it is necessary to develop electronic textbooks. Creation of a high-quality electronic textbook is a complex, laborious process that requires the participation of a number of specialists: editors, designers, programmers. And today, with the transition to mass distance learning, it has become possible to satisfy the need for the rapid development and use of electronic textbooks using iSpring products. With iSpring, a teacher independently can not only record a video for each lecture, but also prepare various interactive tasks and tests, and receive ready-made results of completed tests and assignments. ISpring Learn for Distance Learning allows you to actively lead the learning process and track learning outcomes. As part of the article, we aim to show how to prepare electronic assignments in the modern Kazakh language using iSpring Suite - 9.
Impact of COVID-19 emergency transition to on-line learning onto the international students’ perceptions of educational process at Russian university

The research aims to explore ways of improving adaptability of international first-year university students based on the experience of making the transition from face-to-face learning to various online platforms during the nationwide pandemic lockdown in Russia in spring 2020. The research purpose is to analyze various sociocultural, technical and other challenges faced by international students after the one-day seamless transition to remote learning and their impact on the attendance record, motivation, academic performance and other quantitative and qualitative data. The research topic relevance  is determined  by the substantial rise in popularity of online learning, which had been demonstrating exponential growth even prior to the events of 2020, which further accelerated its expansion. The research methods integrate theoretical analysis of the current trends in online learning and its evolution as well as the strengths, weaknesses, threats and opportunities analysis. Statistical data  was collected  and processed using the University's digital ecosystem. The research data includes the results of the survey consisting of multiple-choice, single-choice and open-end questions, interviews with the students as well as the faculty and the background material. The research sample includes international first-year students from 12 countries with CEFR levels of the Russian language proficiency ranging from A1 to B1. The research findings show that the speed of adaptation to online learning depends on various psychological and technological factors as well as the students’ learning abilities. The findings lead to the conclusion that despite the efforts of the faculty to make this process a seamless one, some factors lie beyond its reach, which makes neutralizing the disadvantages of remote teaching time-consuming. The research results demonstrate the potential ways of improving online learning for all parties concerned by implementing such features as risk management plans, knowledge bases, focusing on making the content user-friendly and introducing other various quality-of-life improvements.
Designing Application For Defect Recording and Handover Of Property Based On Mobile Application by Applying SQLite Technology

Property development companies that produce housing products, high rise dwellings, and office buildings generally have data on the quality of buildings, one of which is obtained during the defect inspection process between developers and consumers before handing over units. Recording data is generally still done manually using a form on a paper. For these conditions, researchers tried to build an application based on mobile apps to digitally record the defect checklist of the dwelling so that the data collected can be processed for the needs of analysis and development strategies. Difficulties encountered during the unit handover process using digital methods on the newly completed property, one of which is the quality of data and internet signals. Mobile apps certainly require a data signal connection to send data to the server. This Android-based mobile apps will implement SQLite technology which allows the recording of transactions to be done locally first, which can then be synchronized into the database server after getting the required internet data connection. SQLite was chosen because it has a relatively small library code unlike relational DBMS in general. SQLite is also easy to use without complex configurations. With the support of the ease of function of SQLite it also allows applications to be integrated with the property sales application system.
DA-Transformer: Distance-aware Transformer

Transformer has achieved great success in the NLP field by composing various advanced models like BERT and GPT. However, Transformer and its existing variants may not be optimal in capturing token distances because the position or distance embeddings used by these methods usually cannot keep the precise information of real distances, which may not be beneficial for modeling the orders and relations of contexts. In this paper, we propose DA-Transformer, which is a distance-aware Transformer that can exploit the real distance. We propose to incorporate the real distances between tokens to re-scale the raw self-attention weights, which are computed by the relevance between attention query and key. Concretely, in different self-attention heads the relative distance between each pair of tokens is weighted by different learnable parameters, which control the different preferences on long- or short-term information of these heads. Since the raw weighted real distances may not be optimal for adjusting self-attention weights, we propose a learnable sigmoid function to map them into re-scaled coefficients that have proper ranges. We first clip the raw self-attention weights via the ReLU function to keep non-negativity and introduce sparsity, and then multiply them with the re-scaled coefficients to encode real distance information into self-attention. Extensive experiments on five benchmark datasets show that DA-Transformer can effectively improve the performance of many tasks and outperform the vanilla Transformer and its several variants.
Animasi Pembuatan Film Animasi 3D Cerita Rakyat Putri Mambang Linau Menggunakan Metode Render Farm

Rendering is the process of the converting objects on the camera into images. Based on the results of the survey to students who worked on the final 3D animation film project, it was obtained a week to render 3D animated films. To overcome the long rendering time, a render farm is used, a collection of computers built to speed up rendering. The implementation of render farm has been successful in previous research with performance results using five clustered computers 60% faster than single computer. However, that research uses a small collection of frames. Therefore, this research will use Putri Mambang Linau’s 3D animation on the render farm to get result that can prove the render farm can be used in making 3D animated films. Based on the performance testing that has been done using 32 clusters 95.48% or 22 times faster than single computer, this shows that render farm can help students in rendering 3D animation film and questionnaires that have been done get result is 81.99% that films made can be introduced to the public.
Cybercriminals Are Actively Exploiting The Coronavirus Pandemic To Gain Entry To Enterprise Networks. Don’t Let Your Company Become The Next Ransomware Headline. Implement The Following Strategies Now!

Malware, short for malicious software, is a generic term used to describe viruses, ransomware, worms, trojans and other harmful computer programs ...
Effects of sample stretching in face recognition

Face stretching is something that can occur intentionally and unintentionally when preparing a face sample for enrollment in a face recognition system. In this paper we assess what affects both horizontal and vertical stretching have on a face recognition algorithms. Basic closed-set identification tests revealed that holistic face recognition algorithms performed poorly compared to feature based recognition algorithms when classifying non-stretched samples against templates based on stretched samples.