Reviewerly/exHarmony huggingface dataset:
Comparative Assessment of the Different Cryptocurrencies Investment Efficiency on the Different Time Periods

Cryptocurrency has appeared recently but become important part of the global financial sector.Some experts predict that cryptocurrency has changed the world forever because of its utility to transfer payments across borders with little cost or delay.Cryptocurrencies are traded with no broker and tracked on digital ledgers.Dramatic changes in the value of the cryptocurrency attract the investors' attention.Some of the have a one-day trading strategy, other investors construct crypto portfolio for a long time periods.The object of this study is value of Bitcoin, Ethereum, and Litecoin on short time periods and long time periods.The subject of the study is the statistical analysis methods to assess the different cryptocurrencies investment efficiency on the different time periods.The aim of the study is to find out what cryptocurrency strategy is better to use for investors.The analysis is carried out on randomly generated periods for 8 samples cryptocurrencies value.The result of the study allows to state that investor does not need to construct crypto portfolio based on its profitability.An effective strategy is to invest money for long time periods in any cryptocurrencies whose utilities meet the investor's requirements.
Noisy quantum amplitude estimation without noise estimation

Many quantum algorithms contain an important subroutine---the quantum amplitude estimation. As the name implies, this is essentially the parameter estimation problem and thus can be handled via the established statistical estimation theory. However, this problem has an intrinsic difficulty that the system, i.e., the real quantum computing device, inevitably introduces unknown noise; the probability distribution model then has to incorporate many nuisance noise parameters, resulting that the construction of an optimal estimator becomes inefficient and difficult. For this problem we apply the theory of nuisance parameters (more specifically, the parameter orthogonalization method) to precisely compute the maximum likelihood estimator for only the target amplitude parameter by removing the other nuisance noise parameters. That is, we can estimate the amplitude parameter without estimating the noise parameters. We validate the parameter orthogonalization method in a numerical simulation and study the performance of the estimator in the experiment using a real superconducting quantum device.
Exploring the Potential Usage of Mobile Technologies Among the Hearing-Impaired Students in Learning English as a Second Language (ESL)

&lt;p&gt;With the emergence of mobile technologies in education, the special needs students are gaining the infinite benefits that could enhance their learning sessions. Numerous studies are conducted on the usage of mobile technologies among the special needs students including their acceptance towards the technologies, its advantages as well as the obstacles on the utilisation of mobile technologies. However, fewer studies are done on the usage of mobile technologies among the hearing-impaired (HI) students. There is a need for more studies on that as the number of hearing-impaired people are increasing continuously around the globe. Hence, the aims of the study are to explore the hearing-impaired students’ perspectives on the usa of mobile technologies in learning ESL. In-depth interviews are conducted among the selected ten hearing-impaired students from four different secondary schools in Malaysia and the notable finding demonstrates the majority of them believed that mobile technologies assist them in learning. This study provides the insights for the education stakeholders, especially the policy makers and inventors to create the solutions and improvise the features of mobile technologies that are more user-friendly towards the hearing-impaired students.&lt;/p&gt;
Importance Estimation from Multiple Perspectives for Keyphrase Extraction

Keyphrase extraction is a fundamental task in Natural Language Processing, which usually contains two main parts: candidate keyphrase extraction and keyphrase importance estimation.From the view of human understanding documents, we typically measure the importance of phrase according to its syntactic accuracy, information saliency, and concept consistency simultaneously.However, most existing keyphrase extraction approaches only focus on the part of them, which leads to biased results.In this paper, we propose a new approach to estimate the importance of keyphrase from multiple perspectives (called as KIEMP) and further improve the performance of keyphrase extraction.Specifically, KIEMP estimates the importance of phrase with three modules: a chunking module to measure its syntactic accuracy, a ranking module to check its information saliency, and a matching module to judge the concept (i.e., topic) consistency between phrase and the whole document.These three modules are seamlessly jointed together via an end-toend multi-task learning model, which is helpful for three parts to enhance each other and balance the effects of three perspectives.Experimental results on six benchmark datasets show that KIEMP outperforms the existing state-ofthe-art keyphrase extraction approaches in most cases.
Dynamic Task Scheduling based on Burst Time Requirement for Cloud Environment

Cloud computing has an indispensable role in the modern digital scenario. The fundamental challenge of cloud systems is to accommodate user requirements which keep on varying. This dynamic cloud environment demands the necessity of complex algorithms to resolve the trouble of task allotment. The overall performance of cloud systems is rooted in the efficiency of task scheduling algorithms. The dynamic property of cloud systems makes it challenging to find an optimal solution satisfying all the evaluation metrics. The new approach is formulated on the Round Robin and the Shortest Job First algorithms. The Round Robin method reduces starvation, and the Shortest Job First decreases the average waiting time. In this work, the advantages of both algorithms are incorporated to improve the makespan of user tasks.
Estimating energy levels of a three-level atom in single and multi-parameter metrological schemes

Abstract Finding the energy levels of a quantum system is a significant task, for instance, to characterize the compatibility of materials or to analyze reaction rates in drug discovery and catalysis. In this paper we investigate quantum metrology, the research field focusing on the estimation of unknown parameters investigating quantum resources, to address this problem for a three-level system interacting with laser fields. The performance of simultaneous estimation of the levels compared to independent one is also studied in various scenarios. Moreover, we introduce the Hilbert-Schmidt speed (HSS), a mathematical tool, as a powerful figure of merit for enhancing the estimation of the energy spectrum. This measure can be easily computed, since it does not require diagonalizing the density matrix of the system, verifying its efficiency to enhance quantum estimation in high-dimensional systems.
Interpretable ontology meta-matching in the biomedical domain using Mamdani fuzzy inference

Ontology meta-matching techniques have been consolidated as one of the best approaches to face the problem of discovering semantic relationships between knowledge models that belong to the same domain but have been developed independently. After more than a decade of research, the community has reached a stage of maturity characterized by increasingly better results and aspects such as the robustness and scalability of solutions have been solved. However, the resulting models remain practically intelligible to a human operator. In this work, we present a novel approach based on Mamdani fuzzy inference exploiting a model very close to natural language. This fact has a double objective: to achieve results with high degrees of accuracy but at the same time to guarantee the interpretability of the resulting models. After validating our proposal with several ontological models popular in the biomedical field, we can conclude that the results obtained are promising.
Blockchain for Development in the Era of the COVID-19 Pandemic

In the context of Information and Communication Technologies (ICT) for Development (ICT4D), several efforts have been carried out over the last decades to help developing countries address their challenges.In times of a pandemic, such as the COVID-19, several of these challenges gain higher priority in development as they may become unmanageable with the developing world's resources.Blockchain is a disruptive technology that could potentially enable the sustainable addressing of some of them, and COVID-19 offers a unique and timely opportunity to operationalize it.Driven by the research question "In what areas can Blockchain help address development challenges during a pandemic?"we aim to understand its applicability in addressing crucial social problems in developing countries.While the literature analysis points towards several promising areas, there are also significant concerns that need to be understood and carefully addressed when considering Blockchain in the context of developing countries.The critical discourse in this work is expected to benefit a wide variety of technology, policy, and research stakeholders.
I-GROUP PRESENCE QUESTIONNAIRE: PSYCHOMETRICALLY REVISED ENGLISH VERSION

I-group Presence Questionnaire (IPQ), which is used to evaluate the mediated experience of presence -especially for virtual reality applications- is originally developed in German and translated to several other languages. However, there is not any psychometric study for these translations including English version, except the Portuguese and Persian translations. We evaluated English translation of IPQ with 36 participants through 12 VR sessions with an overall of 432 samples. Using a partial least squares based factor analysis approach, the original 14-item set is trimmed into 11-items in order to achieve better psychometric qualities. In addition, a covariance based confirmatory factor analysis is executed to compare models. Several indices, even the conservative Cronbach’s alpha indicated that the subscales of 11-item version are reliable, but not the 14-item version. Eliminated items did not lead to a decrease in scales’ sensitivity to identify different levels of Realism, Spatial Presence and Involvement for different virtual environments. Although we provided evidence to remove the items which are identically worded and inversely coded that are causing measurement error, we suggest researchers to employ the 14-items but report the results for both 14-item version and 11-item version, until the psychometric qualities of IPQ in English is confirmed with a larger sample of participants.
Recognition of Russian and Indian Sign Languages Based on Machine Learning

In the paper, we consider recognition of sign languages (SL) with a particular focus on Russian and Indian SLs. The proposed recognition system includes five components: configuration, orientation, localization, movement and non-manual markers. The analysis uses methods of recognition of individual gestures and continuous sign speech for Indian and Russian sign languages (RSL). To recognize individual gestures, the RSL Dataset was developed, which includes more than 35,000 files for over 1000 signs. Each sign was performed with 5 repetitions and at least by 5 deaf native speakers of the Russian Sign Language from Siberia. To isolate epenthesis for continuous RSL, 312 sentences with 5 repetitions were selected and recorded on video. Five types of movements were distinguished, namely, "No gesture", "There is a gesture", "Initial movement", "Transitional movement", "Final movement". The markup of sentences for highlighting epenthesis was carried out on the Supervisely.ly platform. A recurrent network architecture (LSTM) was built, implemented using the TensorFlow Keras machine learning library. The accuracy of correct recognition of epenthesis was 95 %. The work on a similar dataset for the recognition of both individual gestures and continuous Indian sign language (ISL) is continuing. To recognize hand gestures, the mediapipe holistic library module was used. It contains a group of trained neural network algorithms that allow obtaining the coordinates of the key points of the body, palms and face of a person in the image. The accuracy of 85 % was achieved for the verification data. In the future, it is necessary to significantly increase the amount of labeled data. To recognize non-manual components, a number of rules have been developed for certain movements in the face. These rules include positions for the eyes, eyelids, mouth, tongue, and head tilt.
Social Media Based Decision Support Model To Solve Indonesian Waste Management Problem: An Improved Version

Twitter is a commonly used social media and can sometimes picture an entire situation especially environmental issues like waste management. Machine learning and sentiment analysis tools have also been used in many cases around the world and has produced useful results to assist decision making models. In this research Decision Support Model (DSM) and Sentiment Analysis with the help of Naïve Bayes Theorem was used to analyze the waste management case in Indonesia and find out how much improvement is needed in the current situation. The research has found that severe improvements in all of the 5 aspects analyzed is needed to elevate the waste management quality to the next level, especially with a low overall score of 45.29.
Dual-Domain Fusion Convolutional Neural Network for Contrast Enhancement Forensics

Contrast enhancement forensics techniques have always been of great interest for the image forensics community, as they can be an effective tool for recovering image history and identifying tampered images. Although several contrast enhancement forensic algorithms have been proposed, their accuracy and robustness against some kinds of processing are still unsatisfactory. In order to attenuate such deficiency, in this paper, we propose a new framework based on dual-domain fusion convolutional neural network to fuse the features of pixel and histogram domains for contrast enhancement forensics. Specifically, we first present a pixel-domain convolutional neural network to automatically capture the patterns of contrast-enhanced images in the pixel domain. Then, we present a histogram-domain convolutional neural network to extract the features in the histogram domain. The feature representations of pixel and histogram domains are fused and fed into two fully connected layers for the classification of contrast-enhanced images. Experimental results show that the proposed method achieves better performance and is robust against pre-JPEG compression and antiforensics attacks, obtaining over 99% detection accuracy for JPEG-compressed images with different QFs and antiforensics attack. In addition, a strategy for performance improvements of CNN-based forensics is explored, which could provide guidance for the design of CNN-based forensics tools.
Developing Accessible Mobile Applications with Cross-Platform Development Frameworks

We illustrate our experience, gained over years of involvement in multiple research and commercial projects, in developing accessible mobile apps with cross-platform development frameworks (CPDF). These frameworks allow the developers to write the app code only once and run it on both iOS and Android. However, they have limited support for accessibility features, in particular for what concerns the interaction with the system screen reader.
Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.
The Application of Semi Variogram and Ordinary Kriging in Determining the Cohesion and Clay Percentage Distribution in Hilly Area of Sabah, Malaysia

Ordinary Kriging (OK) is one of the geostatistical methods, which were used in the variation types of mapping, which related to the soil. Compliment by semi variogram models, OK has become one of the most sought out method for the digital mapping, which applied Geographical Information System (GIS) as a main approach. Four semi variogram models, which are spherical, exponential, circular and gaussian would be applied to determine the best model for the mapping purposes, with Root-Mean-Squared-Error (RMSE) as a performance indicator. The value of the cohesion and clay percentage will be based according to the related depth. Each semi variogram model will be applied to determine the best model for each depth, whether it is cohesion or clay percentage, and producing a map, as a result. This mapping would be an alternative to the geological mapping, whereby it would show the range of the cohesion and clay percentage values rather than soil types.
Learning Japanese Using Marugoto Japanese Online Course Site (Minato)

A public university has shifted the Japanese language teaching method from using full textbooks to the application of combining textbook and online platforms.This shift is in lieu of the transition in teaching and learning patterns initiated by the rapid development of IT and the effects of pandemic.What motivates the researchers to embark on this study is that with the change of learning face-to-face to online learning, foreign language instructors need to prepare more interesting online materials to ease online teaching and learning.Generally, this study is aimed to introduce yet another online teaching materials for the teaching and learning of Japanese.Specifically, this study is to explore how the students learned Japanese online with the assistance of Minato, employing quantitative methods.A forty-seven-item questionnaire, with questions related to behaviour (13 items), situational (20 items) and cognitive (14 items) factors was administered, and 37 students participated in the survey.Based on the findings, it had been illustrated that out of 47 item questions; eleven (11) items recorded the highest score of 4.6 mean points.Eight (8) of the highest means are items from Situational Factors and three items from Cognitive Factors.The sub-factors for Situational Factors consist of learning about culture and learning about lifestyle.Out of eight (8) items with the highest means; three (3) items from culture and five items from lifestyle.The subfactors for Cognitive Factors are learning vocabulary, learning pronunciation and learning grammar.Out of three (3) items with the highest means consists of two (2) items from learning vocabulary and one item from learning pronunciation.The lowest mean of 3.5 points is from Cognitive Factors and the item is learning grammar.Therefore, this study has proven that teaching and learning using videos and photos are the most effective methods for learning Japanese culture and lifestyle through an online platform, whilst learning grammar still need teachers-students' face-to-face interactions in class.
Economics of education and prospects for its development

The article considers the prospects for the development of the education economy in modern market conditions. The main reason for considering this topic is that in the modern period, this science is immature, since it does not demonstrate the ability to offer effective, efficient, socially feasible and economically viable solutions to educational problems. Accordingly, the main direction of the development of this science should be considered the development of scientific fundamental and technological approaches to solving existing problems in education. One of the tools in this regard should be called educational projects that contribute to the dynamic development of both pedagogy in general and this science in particular. To be recognized as an integrated field of research, the economics of education should assign to pedagogy the role of helping scientists to create formative solutions to the criticisms identified in the course of economic research. Indeed, within the framework of the role that pedagogy can assume, planning in the field of education, the formation of various aspects of personality (economic, financial, social and human) can be developed.
Designing an Asynchronous, Self-Led Aquatic Ecology Field Trip

Due to the COVID-19 pandemic and increasing need to teach students online, aquatic scientists are looking for ways to give students field experiences virtually. Asynchronous, self-led field trips are emerging as a solution. However, due to the varying circumstances surrounding students and the dangers of exploring near water alone, asynchronous field trips need to be designed with equity, inclusivity, and safety in mind. Here, I provide a guide to creating inclusive field trips meant to introduce students to making qualitative scientific observations about aquatic ecosystems. This guide for designing an asynchronous, self-led aquatic ecology field trip explains how to: i) gauge whether this type of activity is suitable for your students, ii) promote safety and equity in choosing field trip sites, iii) build a community of learners while in a virtual setting, iv) prepare students for their individual trips, v) create a step-by-step worksheet to lead students through the activity, and vi) improve the experience for future classes. <em>Primary image:&nbsp;</em>Backyard River: Standing on a bridge looking over the Rillito River flowing through urban Tucson (photo taken by the author).
Reachability is NP-Complete Even for the Simplest Neural Networks

We investigate the complexity of the reachability problem for (deep) neural networks: does it compute valid output given some valid input? It was recently claimed that the problem is NP-complete for general neural networks and conjunctive input/output specifications. We repair some flaws in the original upper and lower bound proofs. We then show that NP-hardness already holds for restricted classes of simple specifications and neural networks with just one layer, as well as neural networks with minimal requirements on the occurring parameters.