Reviewerly/exHarmony huggingface dataset:
Analysis of the Use of Virtual Meeting in the Implementation of Proposal/Thesis Examination During Covid-19 Pandemic

In the early 2020s, Indonesia was shocked by the Coronavirus Disease (Covid-19) outbreak which struck almost all parts of the world.The Indonesian government spontaneously implemented a policy of learning from home, working from home, and worship at home.All face-to-face activities are diverted online.In the field of education, both universities and schools need to strengthen online learning during this time.Universitas Negeri Surabaya has an alternative in carrying out a proposal / thesis seminar by using virtual meetings.The virtual meetings used include: meeting.unesa.ac.id, rv.unesa.ac.id, zoom, google meets, Webex and others.This study aims to determine students' perceptions of the virtual meeting features as a means of online proposal / thesis examination in the middle of a pandemic.This study can be a reference whether the use of virtual meetings as an alternative in carrying out a seminar proposal / thesis is effectively implemented.The research focuses on students' opinions on the usability factor (PU) and convenience factor (PEOU) in virtual meeting features following the Technology Acceptance Model theory.This research was conducted by quantitative methods.The sample used in this study were students of Universitas Negeri Surabaya who were taking the thesis.Data collection techniques using a questionnaire with a Likert scale.Then the results of the questionnaire data are processed using the Structural Equation Model (SEM) with SPSS tools.The results of the study stated that the research indicators related to user satisfaction in utilizing virtual meetings as an alternative for implementing seminar proposals / theses passed the validity, reliability, and linearity tests.Validity test value> r table.The value of the alpha reliability test was between 0.70 -0.90.While the linearity test value> 0.05.
Forking Tweakable Even-Mansour Ciphers

A forkcipher is a keyed, tweakable function mapping an n-bit input to a 2nbit output, which is equivalent to concatenating two outputs from two permutations. A forkcipher can be a useful primitive to design authenticated encryption schemes for short messages. A forkcipher is typically designed within the iterate-fork-iterate (IFI) paradigm, while the provable security of such a construction has not been widely explored.In this paper, we propose a method of constructing a forkcipher using public permutations as its building primitives. It can be seen as applying the IFI paradigm to the tweakable Even-Mansour ciphers. So our construction is dubbed the forked tweakable Even-Mansour (FTEM) cipher. Our main result is to prove that a (1, 1)-round FTEM cipher (applying a single-round TEM to a plaintext, followed by two independent copies of a single-round TEM) is secure up to 2 2n/3 queries in the ideal permutation model.
Collaborative Training of Gans in Continuous and Discrete Spaces for Text Generation

Applying generative adversarial networks (GANs) to text-related tasks is challenging due to the discrete nature of language. One line of research resolves this issue by employing reinforcement learning (RL) and optimizing the next-word sampling policy directly in a discrete action space. Such methods compute the rewards from complete sentences and avoid error accumulation due to exposure bias. Other approaches employ approximation techniques that map the text to continuous representation in order to circumvent the non-differentiable discrete process. Particularly, autoencoder-based methods effectively produce robust representations that can model complex discrete structures. In this article, we propose a novel text GAN architecture that promotes the collaborative training of the continuous-space and discrete-space methods. Our method employs an autoencoder to learn an implicit data manifold, providing a learning objective for adversarial training in a continuous space. Furthermore, the complete textual output is directly evaluated and updated via RL in a discrete space. The collaborative interplay between the two adversarial trainings effectively regularize the text representations in different spaces. The experimental results on three standard benchmark datasets show that our model substantially outperforms state-of-the-art text GANs with respect to quality, diversity, and global consistency.
Methodological recommendations to face an online semester in a catastrophic situation: the case of a computer science program during the COVID-19 lockdown

The pandemic context forced most of the Higher Education Institutions, and therefore their faculties, to face a semester of teaching in an online mode.The universities defined general guidelines and strategies to try to guarantee a minimum quality of the process.However, the literature reports that, depending on its particularities, preparing a semester-long online course requires a design of several months.Therefore, each lecturer worked according to her experience, guidelines, and support she received: "What could be done, was done."This article reports the teaching experience of the 1st semester 2020 of the Computer Science undergraduate program at the Universidad Católica de Temuco.Some lecturers of this undergraduate program had previous experience in online training; hence they designed strategies that differed in teaching, evaluation, and interaction methodologies while using several technologies available to them.A sample of 115 of our students answered a questionnaire to assess the preferences and problems during this semester.The results show a heterogeneous set of clusters.However, they point to a constructivist asynchronous vision in a group of our students that prefer short videos uploaded by their professors, and the resources that, by their means, they find on the Internet, point towards a constructivist asynchronous vision in a group of our students.The results also allowed us to rescue good practices that help in proposing an emergency online teaching model that will guide the design of the second-semester courses.
Evaluasi Keberhasilan Sistem Informasi Akademik dengan Pendekatan Model DeLone dan McLean

Development of academic management needs will increasingly complex, therefore the development of academic information systems must be oriented to user satisfaction as a way to measure the success of the information system. This study aims to analyze the performance of the quality of academic information systems (SIAKAD) and analyze the factors that influence the use, user satisfaction and net benefits of SIAKAD at Sultan Ageng Tirtayasa University. The respondents used in this study consisted of three groups totaling 400 people. The model used in this research is the information system success model from DeLone and McLean. Descriptive analysis is used to explain respondents' perceptions and evaluations of the successful implementation of SIAKAD. SEM PLS analysis is used to analyze the factors that influence the successful implementation of SIAKAD at Sultan Ageng Tirtayasa University. SIAKAD users rated success at the semantic level and technical level included in the good category, while the success at the effectiveness level was still in the good enough category. Hypothesis testing results indicate that the quality of information system quality and service quality has a significant positive effect on the use and satisfaction of users, the use of a significant positive effect on user satisfaction, and the use and satisfaction of users has a positive and significant effect on the net benefits of SIAKAD.
Open Data Challenges in Climate Science

<p dir="ltr">The purpose of this paper is to explore challenges in open climate data experienced by data scientists at the Centre for Environmental Data Analysis (CEDA). This paper explores two of the five V’s of Big Data, Volume and Variety. These challenges are explored using the Sentinel satellite data and Climate Modelling Intercomparison Project phase six (CMIP6) data held in the CEDA Archive. To address the Big Data Volume challenge, this paper describes the approach developed by CEDA to manage large volumes of data through the allocation of storage as filesets. These filesets allow CEDA to plan and track dataset storage volumes, a flexible approach which could be adopted by any data centre. CEDA utilise the implementation of the Climate and Forecast (CF) conventions and standard names within archived data wherever possible to overcome the challenge of Variety. Collaboration from the international science community through contributions to the moderation of CF standard names ensures these data then adhere to the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. Utilising data standards such as the CF standard names is recommended because it promotes data exchange and allows data from different sources to be compared. Addressing these Open Data challenges is crucial to ensure valuable climate data are made available to the scientific community to facilitate research that addresses one of society’s most pressing issues – climate change.
Analysis of Host Resources Utilization by OpenStack in Ubuntu Environment

Cloud computing is one of the frontier technologies, which over the last decade has gained a widespread commercial and educational user base. OpenStack is one of the popular open source cloud management platforms for establishing a private or public Infrastructure-as-a-Service (IAAS) cloud. Although OpenStack started with very few core modules, it now houses nearly 38 modules and is quite complex. Such a complex software bundle is bound to have an impact on the underlying hardware utilization of the host system. The objective is to monitor the usage of system resources by OpenStack on commodity hardware. This paper analyzes the effect of OpenStack on the host machine's hardware. An extensive empirical evaluation has been done on different types of hardware, at different virtualization levels and with different flavours of operating systems comparing the CPU utilization, memory consumption, disk I/O, network, and I/O requests. OpenStack was deployed using Devstack on a single node. The novel aspect of this work is monitoring the resource usage by OpenStack without creating virtual machines on commodity hardware. From the analysis of data, it is observed that standalone machine with Ubuntu server operating system is the least effected by OpenStack and thereby has more available resources for computation of user workloads. Doi: 10.28991/esj-2020-01246 Full Text: PDF
Desktop Voice Assistant Using Natural Language Processing (NLP)

Voice assistants are the great innovation in the field of AI that can change the way of living of the people in a different manner. the voice assistant was first introduced on smartphones and after the popularity it got. It was widely accepted by all. Initially, the voice assistant was mostly being used in smartphones and laptops but now it is also coming as home automation and smart speakers. Many devices are becoming smarter in their own way to interact with human in an easy language. The Desktop based voice assistant are the programs that can recognize human voices and can respond via integrated voice system. This paper will define the working of a voice assistants, their main problems and limitations. In this paper it is described that the method of creating a voice assistant without using cloud services, which will allow the expansion of such devices in the future.
Face Mask Detection System using Deep Learning

In the present scenario due to Covid-19, there is no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed. To identify the person on image/video stream wearing face mask or not. If the person doesn’t wear a mask, the notification will be sent to the respected admin with the help of Python and deep learning algorithm by using the Convolutional Neural Network, Keras Framework and OpenCV.
Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

Recommender systems should be able to handle highly sparse training data that continues to change over time. Among the many solutions, Ant Colony Optimization, as a kind of optimization algorithm modeled on the actions of an ant colony, enjoys the favorable characteristic of being optimal, which has not been easily achieved by other kinds of algorithms. A recent work adopting genetic optimization proposes a collaborative filtering scheme: Ant Collaborative Filtering (ACF), which models the pheromone of ants for a recommender system in two ways: (1) use the pheromone exchange to model the ratings given by users with respect to items; (2) use the evaporation of existing pheromone to model the evolution of users’ preference change over time. This mechanism helps to identify the users and the items most related, even in the case of sparsity, and can capture the drift of user preferences over time. However, it reveals that many users share the same preference over items, which means it is not necessary to initialize each user with a unique type of pheromone, as was done with the ACF. Regarding the sparsity problem, this work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K&lt;&lt;#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users. We call this revised version the Improved Ant Collaborative Filtering (IACF). Experiments are conducted on larger datasets, compared with the previous work, based on three typical recommender systems: (1) movie recommendations, (2) music recommendations, and (3) book recommendations. For movie recommendation, a larger dataset, MoviesLens 10M, was used, instead of MoviesLens 1M. For book recommendation and music recommendation, we used a new dataset that has a much larger size of samples from Douban and NetEase. The results illustrate that our IACF algorithm can better deal with practical recommendation scenarios that handle sparse dataset.
Simulation role play as a contact work with students and its role in their communicative skills development

The article describes the diagnostic and correctional possibilities of using simulation role play with University students. It should be noted, that simulation and a role play allows to immerse students in active controlled communication, where they can express themselves, learn about their hidden capabilities, and develop their skills necessary for more productive functioning as a group or individually. Such play should contain elements of modeling, construction, and transformation. Today, simulation role play offers great opportunities to work with any categories of students, and have a great practical use, as it provides an opportunity to master both verbal and non-verbal forms of communication in various situations, clearly demonstrating the ways and possibilities to apply in practice the knowledge gained. The study showed that the use of simulation role play in the educational process helps to increase significantly the communicative competence of students. Thanks to a 4 months’ simulation role play with students during their studies at the University, they have significantly increased their communication competence, formed new communication skills and communication behavior strategies, and decreased the indicators of "communicative clumsiness". The results suggest that simulation role play as a type of socio-psychological training can be actively used in teaching practice at the University for the development of students ' communication skills, as it involves dialogue, relying on life experience and feelings of younger generation. This is a form of specially organized communication, which may become necessary in the increasing distant "indirect interaction" in our lives through social networks and the Internet.
Fake Near Collisions Attacks

Fast Near collision attacks on the stream ciphers Grain v1 and A5/1 were presented at Eurocrypt 2018 and Asiacrypt 2019 respectively. They use the fact that the entire internal state can be split into two parts so that the second part can be recovered from the first one which can be found using the keystream prefix and some guesses of the key materials.In this paper we reevaluate the complexity of these attacks and show that actually they are inferior to previously known results. Basically, we show that their complexity is actually much higher and we point out the main problems of these papers based on information theoretic ideas. We also check that some distributions do not have the predicted entropy loss claimed by the authors. Checking cryptographic attacks with galactic complexity is difficult in general. In particular, as these attacks involve many steps it is hard to identify precisely where the attacks are flawed. But for the attack against A5/1, it could have been avoided if the author had provided a full experiment of its attack since the overall claimed complexity was lower than 232 in both time and memory.
Voice Technologies to Support Aging in Place: Opportunities and Challenges

Abstract Technology to support aging in place has been a topic of interest. Research indicates that older adults are increasingly using commercially available voice assistants in smart speakers. These devices enable non-visual interaction that does not require extensive expertise with traditional mobile or desktop computers, thus offering new possibilities of access to digital technology. We conducted two different studies with individuals aged 65 years old or above—a three week smart speaker deployment study with individuals who did not use computing devices regularly and a workshop on customizing internet of things technology with tech savvy individuals. Our findings indicate specific ways that these voice technologies might support aging in place, including ease of use and due to their not being identified with aging-specific technologies. We observed that participants consistently used their voice agent for finding online information, particularly health-related, emphasizing the need to revisit concerns about credibility of information with this new interaction medium. And, although features to support memory (e.g., setting timers, reminders) were initially perceived as useful, the actual usage was unexpectedly low due to reliability concerns. Our work provides a basis to understand older adults’ perceptions and usage of current voice technologies. We also identify opportunities for customizing voice technologies to better support aging in place.
Applying Artificial Intelligence Methods for the Estimation of Disease Incidence: The Utility of Language Models

Background: AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We explored the use of machine learning models that leverage contextual information about diseases from unstructured text, to estimate disease incidence. Methods: We used a class of machine learning models, called language models, to extract contextual information relating to disease incidence. We evaluated three different language models: BioBERT, Global Vectors for Word Representation (GloVe), and the Universal Sentence Encoder (USE), as well as an approach which uses all jointly. The output of these models is a mathematical representation of the underlying data, known as "embeddings." We used these to train neural network models to predict disease incidence. The neural networks were trained and validated using data from the Global Burden of Disease study, and tested using independent data sourced from the epidemiological literature. Findings: A variety of language models can be used to encode contextual information of diseases. We found that, on average, BioBERT embeddings were the best for disease names across multiple tasks. In particular, BioBERT was the best performing model when predicting specific disease-country pairs, whilst a fusion model combining BioBERT, GloVe, and USE performed best on average when predicting disease incidence in unseen countries. We also found that GloVe embeddings performed better than BioBERT embeddings when applied to country names. However, we also noticed that the models were limited in view of predicting previously unseen diseases. Further limitations were also observed with substantial variations across age groups and notably lower performance for diseases that are highly dependent on location and climate. Interpretation: We demonstrate that context-aware machine learning models can be used for estimating disease incidence. This method is quicker to implement than traditional epidemiological approaches. We therefore suggest it complements existing modeling efforts, where data is required more rapidly or at larger scale. This may particularly benefit AI-driven digital health products where the data will undergo further processing and a validated approximation of the disease incidence is adequate.
Religious feeling and its influence on the level of moral consciousness of modern student youth

An important task of the society is to develop the moral consciousness of the student youth, since it is they who will further build our country, using concepts based on historical experience about good and evil, justice, honor and dignity. This task is solved using, among other things, the means of science, namely philosophy, ethics, sociology and ecology. At the same time, attempts to look at this topic from the standpoint of psychological science are still relatively rare. Studies of the influence of religiosity on the general moral state of young people are particularly sparse, which leads to the lack of any precisely formulated and justified criteria in the modern understanding of the problem. This position forms, in fact, the task for the psychologist: to identify the structure of moral consciousness typical for a representative of a modern student, to determine the necessary stages and means for the process of moral maturation of a young person receiving higher education. It is particularly important to determine the relationship between personal religious feelings and the formation of mature moral qualities in the student’s psychotype. Studying the works of psychologists who laid the foundations for understanding the processes of moral and spiritual development of the individual, we can imagine the stages of studying a particular process that interests us - from the formulation of a hypothesis to the selection of diagnostic techniques and methods of mathematical processing of results. Without any doubt, the field of view of the study included the definition of value and life-meaning orientations of students, which is ultimately and most important for everyone who takes part in the formation and education of young builders of the future life of our society.