Reviewerly/exHarmony huggingface dataset:
EchoLock: Towards Low-effort Mobile User Identification Leveraging Structure-borne Echos

Many existing identification approaches require active user input, specialized sensing hardware, or personally identifiable information such as fingerprints or face scans. In this paper, we propose EchoLock, a low-effort identification scheme that validates the user by sensing hand geometry via commodity microphones and speakers. EchoLock can serve as a complementary verification method for high-end devices or as a stand-alone user identification scheme for lower-end devices without using privacy-sensitive features. In addition to security applications, our system can also personalize user interactions with smart devices, such as automatically adapting settings or preferences when different people are holding smart remotes. To this end, we study the impact of hands on structure borne sound propagation in mobile devices and develop a user identification scheme that can measure, quantify, and exploit distinct sound reflections in order to differentiate distinct identities. Particularly, we propose a non-intrusive hand sensing technique to derive unique acoustic features in both time and frequency domain, which can effectively capture the physiological and behavioral traits of a user's hand (e.g., hand contours, finger sizes, holding strengths, and holding styles). Furthermore, learning-based algorithms are developed to robustly identify the user under various environments and conditions. We conduct extensive experiments with 20 participants, gathering 80,000 hand geometry samples using different hardware setups across 160 key use case scenarios. Our results show that EchoLock is capable of identifying users with over 94% accuracy, without requiring any active user input.
Analysis of Platforms and Tools of Open Study in the Conditions of Postmodern Education

It is proved that in the conditions of the information society, the analysis of platforms and tools of open education and the use of leading information and communication technologies is important. The article discusses conceptual foundations of "open education" as the successor to the conventional education system. The basic characteristics of open education are considered. The purpose of the article is to systematize and determine the essential characteristics of the world's leading platforms and tools for using information and communication technologies in educational institutions in the process of modernizing the education system in Ukraine. The main object of research is an open education system. The methodology of the study consists of such methods as: the method of historical research, generalization, expert assessment, the questionnaire method, analysis and systematization. The article systematizes and characterizes the world's leading approaches to the use of information and communication technologies in educational institutions in the process of modernizing modern educational systems in accordance with the latest requirements of the information society. The necessity of high-quality filling of the system of "total computer literacy" and total digitalization of the national education system by introducing modern platforms and tools of open education, cloud services, modern technical equipment and software, the entry of the national education system into the global educational space at the institutional, environmental level is substantiated and personal level. It is concluded that today, in relation to the modernization of the national education system, our state faces the difficult task of ensuring that the basic approaches, principles and tools of the educational process meet the requirements of the transition to the era of the information society. The main platforms and digitalization tools of open education are considered. Based on the expert assessment methods and the questionnaire method, the most optimal methods of open education were chosen for higher educational institutions of Ukraine.
Student Emotion Estimation Based on Facial Application in E-Learning during COVID-19 Pandemic

The COVID-19 pandemic occurred in late 2019, and by the beginning of 2020 the entire education system has shifted from traditional teaching methods to online learning systems around the world.COVID-19 reinforces the need to explore online learning and learning opportunities.However, the ability of teachers to recognize and see how individual students engage in online learning is more challenging.Student emotions such as self-esteem, inspiration, dedication, and others that are assumed to be determinants of student success cannot be overlooked.The main objective of this research is to evaluate the emotion of student on e-learning during COVID-19 pandemic using a facial recognition application.This application able to interpretation of facial expressions into extracted emotional states.An image processing approach has been implements in 4 types of emotion which is happy, normal, sad and surprise.Next the image will go through the identifying of emotion type from the static frontal face image.It starts with image acquisition, grayscale conversion and contrast stretching for image pre-processing, Haar Cascade or also known as Viola-Jones technique for face detection, face model technique for eye and mouth localization, skin-color segmentation technique for image segmentation, and Grey-Level Co-Occurrence Matrix (GLCM) for feature extraction.The classification for emotion type is using SVM Regression.The accuracy percentage of emotion classification is calculated.The result showed that SVM Regression has a high accuracy rate of 99.16%.A real-time application will be developed to identify human face emotion instead of static image for future work with additional of speech recognition exploration.
Artists, Artificial Intelligence and Machine-based Creativity in Playform

Artificial intelligence researchers and artists have trained machines and generative processes to produce visually interesting and novel works, thereby devising machinic means of creativity. At Artrendex, Playform was developed as an easy-to-use program specifically to be used by a broad range of artists, from beginners to those with advanced technical skills. This essay focuses on the motivations behind the development of Playform and the early reception and use of it by some artists. Our aim is to better understand both human and machine-based creativity at their intersection in an art generating system such as Playform.
How features of digital text affect reading efficiency and comprehension

As information technologies enter our life, our behaviour changes: we work and study in new ways. Digital text is a technology that plays a unique role in the modern world. Such text dramatically changes the ways we read, search for information and browse through pages. Moreover, we start to work with a text in different modalities: reading and writing at the same time. This is why it is important to study new technologies from the scientific — psychological — point of view. Differences between reading paper and screen-based texts have been studied from the time first screens appeared. These differences stem not only from the use of screens, but also from hypertext and different text structure. We identify two groups of factors that affect the reading process: features of the text itself and readers' individual psychological traits. In this paper, we focus on the former. From this point of view, reading process is affected by font size, face and colour, screen brightness and size, navigation tools and how convenient they are. E-reading is different in terms of how quickly, accurately and attentively we can read, how we navigate through a text and how quickly we get tired. Although by these metrics a digital text is generally inferior to paper one, it excels it in terms of flexibility and potential for tailoring it to a specific task. For instance, you can automatically search through a digital text where you would have to skim through a paper one. The structure of the text, meta information and hyperlinks also affect reading process a lot. The research results here are ambiguous. High-level cognitive processes are unlikely to be directly affected by text features, it seems that they are largely defined by reader's own psychological traits.
NSL-BP: A Meta Classifier Model Based Prediction of Amazon Product Reviews

In machine learning, the product rating prediction based on the semantic analysis of the consumers' reviews is a relevant topic.Amazon is one of the most popular online retailers, with millions of customers purchasing and reviewing products.In the literature, many research projects work on the rating prediction of a given review.In this research project, we introduce a novel approach to enhance the accuracy of rating prediction by machine learning methods by processing the reviewed text.We trained our model by using many methods, so we propose a combined model to predict the ratings of products corresponding to a given review content.First, using k-means and LDA, we cluster the products and topics so that it will be easy to predict the ratings having the same kind of products and reviews together.We trained low, neutral, and high models based on clusters and topics of products.Then, by adopting a stacking ensemble model, we combine Naïve Bayes, Logistic Regression, and SVM to predict the ratings.We will combine these models into a two-level stack.We called this newly introduced model, NSL model, and compared the prediction performance with other methods at state of the art.
ASR for EFL Pronunciation Practice: Segmental Development and Learners’ Beliefs

The current study explored the usefulness of mobile-based automatic speech recognition (ASR) pronunciation practice by investigating a) its effects on the production of four English vowels, and b) learners' perception of ASR as a learning tool.A total of 19 Korean university students produced 28 minimal pair sentences containing the English vowel contrasts /i/-/ɪ/ and /ɛ/-/ae/ (e.g., I said beat, I said bit) at pretest and posttest, and completed six sessions of ASR practice outside of class that involved voice-typing a short text, minimal pairs in sentences, and decontextualized minimal pairs.Results of acoustic analysis of F1 and F2 formant frequencies showed a meaningful improvement in frontness for the vowel /i/, but no changes for the other vowels.Overall, the majority of the participants perceived ASR as useful for pronunciation practice, but some showed skepticism and frustration regarding the current state of the technology.Further discussed are the problems and limitations that EFL learners experienced during the ASR training.
Artificial Intelligence and Civil Liability

Although modern innovations already provide an artificial intelligence-driven devices or software’s capability to function fully autonomously without the involvement of any person, the issue of the specificity of the application of civil liability is, inter alia, weighed at the European Union level. This is linked to the current legal framework of the European Union and its Member States, which has been developed and adopted over a period of time when the operation of devices or software has requested at least an indirect involvement of a person in order to execute a specific task. In contrast, contemporary technological achievements in the design of artificial intelligence provide the ability of an artificial intelligence-driven device and software to take independent decisions regarding the conduct of a particular activity, or, on the contrary, on abstaining from carrying out a particular activity, without any person’s involvement. In such circumstances, there is a need to review and reassess the content of the existing regulation with regard to the application of civil liability both in the fault liability and in the model of strict liability (including product liability), and to establish certainty in the conceptual understanding of artificial intelligence and its isolation from simple process automation.
The Effectiveness of Mobile Assisted Language Learning (MALL) on ESL Listening Skill

Using mobile technology in English learning and teaching has been on the rise all over the world over the past few decades and hence, has received considerable attention and importance from academics in recent years. As a result, several experimental studies have been carried out about the use and effectiveness of mobile phones in the teaching/learning process. However, there have been only a few studies on mobile-assisted listening comprehension. This study aims to explore whether Mobile Assisted Language Learning (MALL) is effective in teaching/learning listening skills to the students of university-level English language programs and could better enhance students’ listening ability. It also endeavors to assess why some MALL strategies/techniques are more effective than others. This study uses a qualitative research method. It exclusively uses the relevant secondary materials available on the broader topic- the use and efficacy of mobile phones in teaching/learning listening skills. The results indicated that the MALL is meaningfully efficacious in teaching/learning ESL/EFL listening skills. Therefore, using appropriate strategies could positively contribute to bringing about better learning. Besides outlining a brief overview of MALL, the study also recommends some practical and useful stratagems that ESL/EFL educators can use while designing MALL listening tasks/activities.
A New Hybrid BA_ABC Algorithm for Global Optimization Problems

Bat Algorithm (BA) and Artificial Bee Colony Algorithm (ABC) are frequently used in solving global optimization problems. Many new algorithms in the literature are obtained by modifying these algorithms for both constrained and unconstrained optimization problems or using them in a hybrid manner with different algorithms. Although successful algorithms have been proposed, BA’s performance declines in complex and large-scale problems are still an ongoing problem. The inadequate global search capability of the BA resulting from its algorithm structure is the major cause of this problem. In this study, firstly, inertia weight was added to the speed formula to improve the search capability of the BA. Then, a new algorithm that operates in a hybrid manner with the ABC algorithm, whose diversity and global search capability is stronger than the BA, was proposed. The performance of the proposed algorithm (BA_ABC) was examined in four different test groups, including classic benchmark functions, CEC2005 small-scale test functions, CEC2010 large-scale test functions, and classical engineering design problems. The BA_ABC results were compared with different algorithms in the literature and current versions of the BA for each test group. The results were interpreted with the help of statistical tests. Furthermore, the contribution of BA and ABC algorithms, which constitute the hybrid algorithm, to the solutions is examined. The proposed algorithm has been found to produce successful and acceptable results.
iobes: Library for Span Level Processing

Many tasks in natural language processing, such as named entity recognition and slot-filling, involve identifying and labeling specific spans of text. In order to leverage common models, these tasks are often recast as sequence labeling tasks. Each token is given a label and these labels are prefixed with special tokens such as B- or I-. After a model assigns labels to each token, these prefixes are used to group the tokens into spans. Properly parsing these annotations is critical for producing fair and comparable metrics; however, despite its importance, there is not an easy-to-use, standardized, programmatically integratable library to help work with span labeling. To remedy this, we introduce our open-source library, iobes. iobes is used for parsing, converting, and processing spans represented as token level decisions.
Toward Digital Transformation of Processes in Legal Metrology for Weighing Instruments

The digital transformation of sovereign processes is a driving force to streamline and innovate processes for measuring instruments under legal control.Providing trust is the essential purpose of Legal Metrology and still a challenging task in the digital domain.Taking the strict legal framework into account, a distributed software architecture is presented that offers privacy, security and resilience.At the same time, the platform approach seamlessly integrates existing public and private infrastructures.Furthermore, a service hub is created with interdependent services that support the digital transformation of paper-based processes, such as verification and software update.Exemplary, these two central use cases are introduced, and its requirements and implementation approach are described.The main goal is to provide the same level of trust and security, by developing new digital concepts, infrastructure and remote processes for a unified digital single market.
PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: A Real-Life Implementation on a Large Scale Pilot

The goal of the paper is to develop an online forecasting procedure to be adopted within the H2020 InteGRIDy project, where the main objective is to use the photovoltaic (PV) forecast for optimizing the configuration of a distribution network (DN). Real-time measurements are obtained and saved for nine photovoltaic plants in a database, together with numerical weather predictions supplied from a commercial weather forecasting service. Adopting several error metrics as a performance index, as well as a historical data set for one of the plants on the DN, a preliminary analysis is performed investigating multiple statistical methods, with the objective of finding the most suitable one in terms of accuracy and computational effort. Hourly forecasts are performed each 6 h, for a horizon of 72 h. Having found the random forest method as the most suitable one, further hyper-parameter tuning of the algorithm was performed to improve performance. Optimal results with respect to normalized root mean square error (NRMSE) were found when training the algorithm using solar irradiation and a time vector, with a dataset consisting of 21 days. It was concluded that adding more features does not improve the accuracy when adopting relatively small training sets. Furthermore, the error was not significantly affected by the horizon of the forecast, where the 72-h horizon forecast showed an error increment of slightly above 2% when compared to the 6-h forecast. Thanks to the InteGRIDy project, the proposed algorithms were tested in a large scale real-life pilot, allowing the validation of the mathematical approach, but taking also into account both, problems related to faults in the telecommunication grids, as well as errors in the data exchange and storage procedures. Such an approach is capable of providing a proper quantification of the performances in a real-life scenario.
Longitudinal and Transversal Elasticity of Natural and Artificial Materials for Musical Instrument Reeds

The reed is the primary component in single-reed woodwind instruments to generate the sound. The airflow of the player's mouth is the energy source and the airflow is modulated by the reed. The oscillations of the reed control the airflow. Traditionally, instrument reeds are made out of natural cane (Arundo Donax), but in efforts to overcome variability problems, synthetic reeds have been introduced. Previous investigations mainly focused on natural cane reeds and direct elasticity measurements did not discriminate between elasticity moduli along different directions. In order to obtain the mechanical properties along the direction of the reed fibres and in the orthogonal direction separately, a three-point bending testing setup was developed, which accommodates the small samples that can be cut from an instrument reed. Static moduli of elasticity were acquired in both directions. Much higher ratios between longitudinal and transversal moduli were seen in the natural cane reed as compared to the artificial reeds. Wet natural reeds showed a strong decrease in moduli of elasticity as compared to dry reeds. Elasticity was significantly higher in artificial reeds. The force-displacement curves of the wet natural reed show hysteresis, whereas the artificial materials did not. In the cane reed, higher energy losses were found in the transversal direction compared to the longitudinal direction.
Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.
Individualized New Teaching Mode for Sports Biomechanics based on Big Data

Sports biomechanics is an applied discipline with relatively strong theoretical knowledge. At present, it is used as an analysis means in exercise training in various countries and plays a huge promotion role for the development of competitive sports and sports science. However, phenomena, such as single-teaching methods, fixed-teaching thinking, backward-teaching environment, and hardware, still exist in the current education for sports biomechanics. For such phenomena, we perform individualized IRDC (Internet + retrieval literature + big data + cloud) teaching by using the Internet big data analysis and considering different characteristics of every student and apply it in online teaching of sports biomechanics course. We base our proposed individualized IRDC teaching mode on postmodern curriculum theory. According to four features, namely, rich, recursive, relational, and rigorous, we propose an individualized IRDC online teaching mode in this study. Moreover, we apply a new wireless telemetry surface EMG tester as a learning tool of practical teaching in the teaching mode, which we use to acquire Internet information data of online course and induce a real time communication between teachers and students. Finally, we adopt the principal component analysis to develop evaluation indices, including expert evaluation in and out of school and peer teacher evaluation, for the teaching mode. We find through teaching practice that the proposed individualized IRDC teaching mode can make the best of advantages of big data teaching, help teachers implement targeted individualized teaching, and contribute to the improvement of students' academic performance and comprehensive qualities.
The Possibilities of Improving Communication Skills in the Training of Engineering Students

New graduates have to meet several expectations on the labour market. Besides their professional knowledge, important skills include good communication, problem-solving, creativity, independence, organisation, leadership, decisive action, dynamism, stress tolerance, analysis, precision, openness, flexibility, co-operation, motivation, digital competence, etc. How can higher education prepare young people for the expectations of the labour market? How can students’ social competences and communication skills be improved? Our paper provides methodological recommendations on improving engineering students’ communication skills, based on an empirical study.