Reviewerly/exHarmony huggingface dataset:
Opinion Clustering About Mobility Decisions

This study aims to show the profile of people about the factors that influence decision-making about public and individual transport. First, a survey was carried out to determine several critical factors, and then an online form was created with these criteria. After obtaining the answers, a study was performed with the k-Modes technique to get the profiles. Several statistical analyses for a better understanding of the data were defined. Three different models were built. After a study developed by clustering, it was concluded that there are five groups of people. Those that consider that all variables are essential or not necessary (extremes). Those that consider that all variables have medium importance. The weighted ones present a more excellent dispersion of values and are selective for priority level five. And then, there is the group with the most answers around the medium/low values. The final degree of importance was obtained by the general average of responses and the correlation average. Nine variables were identified, which respect the defined range of values, mean response greater than 3,431 and indicate correlation more significant than 0,228, for degree five of importance.
Auto Machine Learning-Based Approach for Source Printer Identification

This study investigates the applicability of the Auto Machine Learning-based approach (AutoML) for analyzing microscopic printed document images to attribute that document to its source printer. In this perspective, AutoML, a new rising star of machine learning in practice, has shone brightly as it can satisfy the demand of Machine Learning practitioner communities. In this work, three candidates from popular Machine Learning models and two representatives from AutoML are nominated for a competition. The challenges of traditional methods and the merits of applying AutoML are highlighted through the experiments. Especially the power of ensemble methods to achieve the best possible model for our experimental dataset. Furthermore, the learnability of AutoML to the different levels of uncertainties of printed patterns is also recognized.
HyFarM: Task Orchestration on Hybrid Far Memory for High Performance Per Bit

Tapping into secondary memory resources, i.e., far memory (FM), has shown huge potential to improve the cost-efficiency of data centers. Recent advances in both storage-based vertical FM and network-based horizontal FM have raised new questions about leveraging hybrid FM tiers to achieve the best performance per bit of memory. It is still unclear how to efficiently place tasks when far memory access is enabled.In this work, we propose HyFarM, a novel task management strategy for hybrid FM clusters. We analyze FM sensitivity and cooperatively co-locate tasks to enable high utilization and scalability. Further, by tapping into dynamic memory adaption within and across servers, our strategy allows one to consistently deliver high performance on memory-intensive tasks. We evaluate our design with a heavily instrumented testbench. Compared with the state-of-the-art designs, HyFarM respectively improves memory utilization and the overall performance per bit (PPB) by up to 17.6% and 20.5%, with minor overhead.
Towards Idea Mining: Problem-Solution Phrase Extraction from Text

This paper investigates the feasibility of problem-solution phrases extraction from scientific publications using neural network approaches. Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM-CRFs) and Bidirectional Encoder Representations from Transformers (BERT) were evaluated on two datasets, one of which was created by University of Cambridge Computer Laboratory containing 1000 positive examples of problems and solutions (UCCL1000) with the corresponding phrases annotated. The F1-scores computed on the UCCL1000 dataset indicate that BERT is an effective approach to extract solution phrases (with an F1-score of 97%) and problem phrases (with an F1-score of 83%). To test the model’s robustness on a different corpus with a different annotation scheme, a dataset consisting of 488 problem-solution samples from the Conference on Neural Information Processing Systems (NIPS488) was collected and annotated by human readers. Both Bi-LSTM-CRFs and BERT performances were dramatically lower for NIPS488 in comparison with UCCL1000.
Overview of Zero-Knowledge Proof and Its Applications in Blockchain

The rapid development of blockchain technology applications and the increased demand for data security has greatly driven the research on privacy protection. Zero-knowledge proof is a method that can be used to verify the correctness of data while allowing the provers not to reveal confidential information. In this paper, we first sort out the development process of zero-knowledge proofs, classify them from a model perspective and compare the performance of various protocols. Then we explore the applications of zero-knowledge proofs in privacy transactions and scaling in blockchain and analyze them with specific application cases. Finally, we analyze and summarize the trends of zero-knowledge proofs and provide an appropriate outlook on future improvement directions.
Generating Synthetic Images &amp; Data to Improve Object Detection in CCTV Footage from Public Transport

Passenger behaviour on public transport has become a source of great interest in the wake of the COVID-19 pandemic. Operators are interested in employing new methods to monitor vehicle utilisation and passenger behaviour. One way to do this is through the use of Machine Learning, using the CCTV footage that is already being captured from the vehicles. However, one of the limitations of Machine Learning is that it requires large amounts of annotated training data, which is not always available. In this poster, we present a technique that uses 3D models to generate synthetic training images/data and discuss the effect that training with the synthetic data had on the Machine Learning models when applied to real-world CCTV footage.
Research on Computer Aided English Teaching

With the development of modern information technology, the development of educational ideals, and the reform of English education, the education of Japanese language is increasingly valued by people. By using a computer assisted education system in the education, we can promote the expression of the sensibility material, create the best situation and acquire the best educational effect. The author has better teaching English proficiency by comparing and analyzing the advantages and disadvantages of traditional professor and computer aided instruction.
Chapter 4. Quantitative Analysis

Quantitative analysis in Energy-Dispersive XRF (EDXRF) has two primary components: the quantification of standardized units from spectra, and the use of XRF data in hypothesis testing. To address quantification, the authors survey empirical and fundamental algorithms which are frequently used in pXRF calibrations. To illustrate hypothesis testing in quantitative XRF analysis, an example of Catawba clay typology was used.
2. Demonization and Differentiation

Abstract The study of public opinion towards Muslim minorities in contemporary liberal democracies where majorities are not themselves Muslims needs to start by highlighting the persistence of hatred and intolerance. There is ample evidence from politics and society in Western Europe and North America that right-wing extremism is on the rise and that this ideology of hate increasingly has vilified Muslims. A close inspection of the free text associations with the word “Muslim” of more than 1,000 randomly recruited Norwegian citizens confirms the existence and vicious nature of both demonization and Islamophobia. Still, the largest share of free associations hint that differentiation may be more common than demonization and Islamophobia. A differentiation experiment embedded in a simultaneous survey conducted in France, Germany, Norway, Sweden, and the Netherlands confirms both that discrimination against Muslims persists and that differentiation is far more common than demonization.
Bitcoin Measurement in Accounting: A Theoretical Analysis

Coins appeared many centuries ago with the goal of facilitating the exchange of objects between people, but this means of exchange fell out of the hands of states that had control over their currencies mid-2008, until a new type of currency appeared, the first safe and functional cryptocurrency that did not need to have any government intervening, Bitcoin, a decentralized cryptocurrency that has a manufacturing limit and is becoming increasingly relevant in the current scenario. Accounting in turn has a new challenge of measuring and identifying the new asset in a way that best fits its classification. This paper aims to make a comparison between other scientific papers related to the measurement of the Bitcoin cryptocurrency to obtain a better understanding of how the topic is being addressed. It was concluded that standardization for the registration of these assets is indispensable, and that the main difficulty concerns encountered the reference for quotation in reais and the comparability of information. It is possible to consider that financial operations with cryptocurrencies are similar to operations with foreign currency. The analysis is made assuming the need for classification of Bitcoin and similar assets.
Using Anime to Enhance Learning Motivation and Japanese Listening Skills

Teaching and learning activities Japanese listening skills need the motivation to support learning. However, learning motivation sometimes fluctuates, and the use of learning methods and media is not precise. This study aims to analyze the level of motivation and listening ability of students after using anime learning media. This study used a qualitative method with a note-taking technique and the sample used in this study are 16 students Departement of Japanese language in Bandung State West Java. The results of data showed that the use of anime in listening comprehension learning was able to increase learning motivation and listening skills. This is evidenced by the activities carried out by students when learning takes place and the value of assignments obtained by students has increased.
USE OF MODERN MUSIC AND COMPUTER TECHNOLOGIES IN THE DISCIPLINE STUDY «CHORAL ARRANGEMENT»

Modernization of the education system of Ukraine involves changing the content of the training process for future professionals in the field of computer music. Taking into account the specifics of the training of music teachers, the approach to the choice of modern educational tools is changing. This allows us to emphasize the features of professional music education in Ukraine. The urgency of this topic is due to the widespread use of computers in modern music and pedagogical practice, a significant amount of scientific work in the field of theory of computer technology and its insufficient use in the educational and creative process of students of higher education institutions. The aim of this article is to reveal and analyze modern approaches to the use and application of music and computer technology in the study of choral arrangement and their important impact on the quality of training of future music teachers. The article describes the stages of learning that take place in the study of the discipline «Choral Arrangement» and focuses on the need to introduce innovative approaches, methods, forms and teaching aids using different types of technical means, namely: computer, synthesizer, electronic textbooks, Internet technologies and modern music and computer programs. An important place in the analytical review of the literature was occupied by published materials on the use of computer technology in music education. An analysis of the main research and publications on this issue, as well as the authors’ own observations, led to the conclusion that the use of modern music and computer technology is essential in choral arranging classes. Activation of students’ independent work in performing choral arrangements with the help of computer programs «Music Editor» and computer music programs for recording, processing and arranging choral works helps to intensify learning and interest of students in their future profession.
Embedded DevOps

If you’ve been paying any attention to the software industry over the last several years, you know that DevOps has been a big industry buzzword. DevOps is defined as a software engineering methodology that aims to integrate the work of software development and software operations teams by facilitating a culture of collaboration and shared responsibility. In addition, DevOps seeks to increase the efficiency, speed, and security of the software development and delivery process. In other words, DevOps promises the same dream developers have been chasing for more than half a century: better software, faster. DevOps encompasses a lot of agile philosophy and rolls it up into a few actionable methodologies, although if you read the highlights, it’ll give you that warm and fuzzy feeling with few obvious actionable items.
Innovative ICT-Based Strategies Used by Teachers for the Development of Social Competences in Business Administration Students

This study is developed with the aim of diagnosing the innovative strategies based on ICT used by teachers for the development of social skills in business administration students; which were applied during the COVID 19 pandemic as a response to the change of modality launched by higher education institutions. In this case, the present study was located in the positivist paradigm, with a descriptive level, with a non-experimental, transactional field design. The sample is taken from a group of 25 teachers, who teach in the business administration program of a private university located in the city of Barranquilla, Colombia. A questionnaire consisting of a total of 15 items applied to this sample, which was validated by experts and its reliability was determined by means of the Cronbach's Alpha coefficient, yielding a value of 0.87. As a result, an average of 2.86 was obtained in the variable strategies for the development of social competences; located in the category neither appropriate nor inappropriate. These results show an important level of shortcomings in the implementation of innovative strategies based on ICT used by teachers for the development of social skills in business administration students.
SISTEM INFORMASI LAPORAN LABA RUGI PADA UD. KOPI AYAM RAS JAMBI

This study discusses the problems that occur in UD. Kopi Ayam Ras Jambi which is related to accounting records through the calculation of the income statement still uses the manual method that has not used a computerized system that is in accordance with accounting standards. From this, this situation makes it difficult for management to calculate the income statement every month. The purpose of this study is to create an Income Statement System using the single-step method, which is expected to assist management in calculating the company's profit and loss for each period. The profit and loss statement using this single step can make it easier for management to calculate the income, costs, and profit and loss generated each period. So that the management of UD. Kopi Ayam Ras Jambi can find out in real terms the company's profit and loss for each accounting period. The results of this system are expected to have an impact on efficiency and effectiveness in UD operations. Kopi Ayam Ras Jambi gets an accurate profit and loss report through maximum results.&#x0D; Keywords: Profit-Loss, Information System, Accounting, Computerized System
STUDENTS PERCEPTION TOWARD DESCRIPTIVE REVIEW ACTIVITY ON EXTENSIVE READING CLASS OF ENGLISH DEPARTMENT OF WIDYA GAMA MAHAKAM UNIVERSITY

Reading is one of four skills that must be mastered for English as Foreign Language (EFL) learners. Extensive reading is one of class that students need to take to get bachelor degree in English Department of University of Widya Gama Mahakam Samarinda. Extensive reading goal is to create a fun reading for students, to boost their interest in reading especially English reading. In this academic year, the students of 2021 took their extensive reading class in pandemic era therefore they need to take this class through online proccess. This research aims to find out what is studentsâ€™ feeling after/ whilst reading their favorite English books, novels and short stories in Extensive Reading class. The result reveals that most of students have positive perception towards this activity. Some difficulty faced by a few students in this activity are the difficulty to read the novel even thought this activity bring some positive effect to their study including learning about new vocabularies and motivating them to be independent in learning. They also hope that this descriptive review activity supports them in English reading and they also expect that they would have some other activities to engage fun environtment on their class.
Autonomous Evaluation Method of Unmanned Equipment based on OODA-I Framework

Aiming at the current problems of fuzzy concepts and different standards of autonomous evaluation of unmanned equipment, a universal evaluation method was explored. Firstly, the definition of unmanned equipment autonomy and the development status of evaluation methods are discussed. Secondly, based on the Observation., Orientation, Decision and Action (OODA) fighting loop to expand the OODA-Interaction (OODA-I) autonomous evaluation framework, adopt qualitative analysis and quantitative description method to quantify the evaluation index, the combination of clear scoring method, the evaluation index system of unmanned equipment autonomy is obtained. Then, Extension Analytic Hierarchy Process (EAHP) is introduced to get the weight of each index. Finally, according to the evaluation index system, index weight and score value, the autonomy of unmanned equipment is evaluated as a whole. A case study shows that this method can evaluate the autonomy of unmanned equipment quantitatively.
The Effectiveness Smart Chart to Improve the Students Understanding in Linguistic

Difficult in understanding the saintific book, the monoutone learning method and uninteresting way to learn the Linguistic was the the classic problem which never resolved up to now. The purposes of this research are to cover the students’ problems in understanding Morphemes and Syntax Material based in virtual learning, reduce the students’ pressure and Increase the students’ motivation to learn Morphemes and Syntax material using Smart Chart. In implication, this reseach using experimental research with one group pre-test and post-test. The research was done for the English Department Students by using population and sampe is 32 students whose taken Morpheme and Syntax subject. This research designed was suitable which education atmoshere atmosphere in this era. The technique of sampling is using purposive sampling as the collecting data. Even the lecturer must be working hard and need more time to prepare the learning process, the students show the value table is 1,696 and value of t-test is 20,42. It means that Smart Chart Technique is effective and the students got good achievement.
Evolutionary optimisation with outlier detection-based deep learning model for biomedical data classification

In recent times, large amount of medical data is being generated by various sources such as test reports, medications, etc. Due to the recent advances of machine learning (ML) and deep learning (DL) models, medical data classification (MDC) remains a crucial process in the healthcare sector. This study introduces a new hyperparameter tuned convolutional neural network-recurrent neural network (HPT-CNN-RNN) model for medical data classification. The proposed HPT-CNN-RNN model includes pre-processing step to transform the actual healthcare data into useful format. Besides, SVM-SMOTE approach was executed to handle the class imbalance problems. In addition, outlier detection process is performed using extreme gradient boosting (XGBoost) model. Moreover, bacterial foraging optimisation algorithm (BFOA) with CNNRNN model is employed to categorise medical data. Furthermore, the BFOA is utilised to optimally choose the hyperparameter values of the CNNRNN model. The experimental outcomes designated the better performance of the HPT-CNN-RNN model over the other methods.
OCTIS 2.0: Optimizing and Comparing Topic Models in Italian is Even Simpler!

OCTIS is an open-source framework for training, evaluating and comparing Topic Models. This tool uses single-objective Bayesian Optimization (BO) to optimize the hyper-parameters of the models and thus guarantee a fairer comparison. Yet, a single-objective approach disregards that a user may want to simultaneously optimize multiple objectives. We therefore propose OCTIS 2.0: the extension of OCTIS that addresses the problem of estimating the optimal hyper-parameter configurations for a topic model using multi-objective BO. Moreover, we also release and integrate two pre-processed Italian datasets, which can be easily used as benchmarks for the Italian language.
A Multiscale Windows Deep Learning Aproach For Sensor-Based Human Activity Recogniton

Deep learning for sensor-based Human activity recognition (HAR) is a popular research topic and can be widely used in many real-life applications. By far, most HAR deep learning researches use a fixed-size window to segment sensor data streams into samples of the same length, then perform activity recognition. However, in the real world, the length of activity sequences could be different, fixing window size can not segment sensor data stream very well and could impair recognition performance. In this paper, we propose a multi-scale window deep learning approach for HAR, which generates multiple windows of different lengths centered on each unit of the feature sequence. We conduct experiments on three popular benchmark datasets. The results show that our proposed method outperforms the state-of-the-art performance.
Audio-Video Capture, Conversion, and Editing Software

The prevalence of video hosting sites such as YouTube affords the opportunity for small businesses to get their message out in a way not possible in the not-too-distant past. It’s possible to digitize your old radio commercials from cassette tapes or make your own commercials, tutorials, or slideshows for posting on the Web. For the business owner who is a novice when it comes to working with audio and video, there are some no-cost options for capturing, converting, and editing audio and video files.
Integrate Blockchain with Cloud Based Architecture to Prevent Phishing Attack

The confidentiality of user data and files is crucial in cloud computing. Recently, Multiple phishing attacks have spread. Moreover, customers are tricked into giving vital information, such as credit card numbers and passwords, and enabling hackers access to their devices. To combat phishing assaults, this study suggests a blockchain with cloud architecture. The primary purpose is to analyse how cloud-based phishing assaults occur and highlight blockchain technology's significance in cloud computing. The project attempts to develop a safe data-sharing environment within a cloud computing architecture. The project will create a blockchain-based solution that works with cloud architecture to reduce phishing attacks and provide security benefits based on blockchain technology.