Reviewerly/exHarmony huggingface dataset:
Augmented Reality to Engage Visitors of Science Museums through Interactive Experiences

In the last years, interactive exhibitions based on digital technologies are becoming widely common, thanks to their flexibility and effectiveness in engaging visitors and creating memorable experiences. One of the topics in which digital technologies can be particularly effective is the communication of abstract concepts that are difficult for the human mind to imagine. An emblematic example is the astronomy discipline, which requires us to imagine and understand phenomena far away from our everyday life. In this paper, the authors present a research project, MARSS, in which digital technologies are used effectively to enhance the Users&amp;rsquo; Experience of the Museo Astronomico di Brera located in Milan. Specifically, the MARSS project aims at designing and developing a new digital journey inside the museum to allow different categories of visitors to enjoy the exhibition in an engaging and interactive way. The paper presents the design and development phases of the experience and its evaluation with users. The results of the evaluation indicate that the digital interactive experience has been appreciated by users and is successful in translating the content of high scientific value into more engaging and easily understandable elements.
Politeness Strategies on Students’ Responses to Teacher’s Online Announcements

The shift of the usual operations in education from face-to-face to distance learning due to the COVID-19 pandemic has made the Google classroom one of the online learning modalities. The study aims to identify the politeness strategies employed in students' responses to the Bukidnon State University teachers' Google classroom announcement within three semesters. It also explores the significant difference between genders in employing politeness strategies. The study applied a mixed-method research design. The findings show that the students employ four politeness strategies of Brown and Levinson (1987): Positive, Negative, Bald-On-Record, and Off-Record Strategies. In addition, the male students frequently use negative politeness strategies, which contradicts Lakoff (1975) claims that women's language consists of hedges, apologies, and indirect requests, a negative politeness strategy. Thus, determining significant differences in politeness strategies among students needs other factors such as cultural differences, gender language, gender performativity, and teachers' announcement to which the students respond.
Extracting automata from recurrent neural networks using queries and counterexamples (extended version)

We consider the problem of extracting a deterministic finite automaton (DFA) from a trained recurrent neural network (RNN). We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal DFA describing the state dynamics of a given RNN. We use Angluin’s $$\hbox {L}^{*}$$ algorithm as a learner and the given RNN as an oracle, refining the abstraction of the RNN only as much as necessary for answering equivalence queries. Our technique allows DFA-extraction from the RNN while avoiding state explosion, even when the state vectors are large and fine differentiation is required between RNN states. We experiment on multi-layer GRUs and LSTMs with state-vector dimensions, alphabet sizes, and underlying DFA which are significantly larger than in previous DFA-extraction work. Aditionally, we discuss when it may be relevant to apply the technique to RNNs trained as language models rather than binary classifiers, and present experiments on some such examples. In some of our experiments, the underlying target language can be described with a succinct DFA, yet we find that the extracted DFA is large and complex. These are cases in which the RNN has failed to learn the intended generalisation, and our extraction procedure highlights words which are misclassified by the seemingly “perfect” RNN.
HLF-Kubed: Blockchain-Based Resource Monitoring for Edge Clusters

&#x0D; &#x0D; &#x0D; In the past several years, there has been an increased usage of smart, always- connected devices at the edge of the network, which provide real-time contextual information with low overhead to optimize processes and improve how companies and individuals interact, work, and live. The efficient management of this huge pool of devices requires runtime moni- toring to identify potential performance bottlenecks and physical defects. Typical solutions, where monitoring data are aggregated in a centralized manner, soon become inefficient, as they are unable to handle the increased load and become single points of failure. In addition, the resource-constrained nature of edge devices calls for low-overhead monitoring systems. In this paper, we propose HLF-Kubed, a blockchain-based, highly available framework for monitoring edge devices, leveraging distributed ledger technology. HLF-Kubed builds upon Kubernetes container orchestrator and HyperLedger Fabric frameworks and implements a smart contract through an external chaincode for resource usage storing and querying. Our experimental results show that our proposed setup forms a low-overhead monitoring solution, with an average of 448 MB of memory and 6.8% CPU usage, while introducing 1.1s end-to- end latency for store operation and 0.6s for ledger querying respectively.&#x0D; &#x0D; &#x0D;
A Parametric Model for Audio-Visual Instrument Design, Composition and Performance

This chapter proposes a parametric model that is useful in audio-visual instrument design, composition and performance. We can draw a separation between those activities, but in practice that separation might not be so obvious: ultimately, the iterative creation process must always consider the final, global experience. Derived from a perceptual approach, the model is applicable to the broad diversity of aesthetical options and technical platforms. One can equally discard part of the parameters to analyse recorded audio pieces and films. On the one hand, the model enables the separate analysis of performer-instrument interaction, sound, image, audio-visual relationship and physical setup. On the other, it enables the analysis of how the combination conducts the audience’s experience. The chapter begins by presenting each parameter independently, while illustrating their use with a range of artistic examples. It then explains how their combination facilitates the analysis of expression, of the relative strength of sound and image and of the audience’s feeling of presence. Finally, it demonstrates how the model can be used in creative practice, showing its usefulness as a compositional tool.
Bank Management System Using Blockchain Technology

A blockchain is a decentralized, ordered, and immutable ledger that permits a recording of transactions over a network, wherein digital information is stored in a public shared database. Bitcoin utilizes blockchain technology to maintain the records securely. The banks are stimulated with the aid of using economic and digital transformation and financial innovations. Blockchain technology is fundamental technology with promising application in the financial area. The paper includes the architecture and different features of blockchain technology and how it functions using a consensus algorithm. An angular frontend application is built to visualize and interact with JavaScript blockchain implementation.
Perception of government employees on the use of biometric technology in determining a person’s ethnic group in Nigeria

Selected biometric technologies such as iris, face, gait, voice, etc. are widely used for the recognition of individuals. This paper presents a biometric technology perception by members of staff from government agencies in Nigeria, namely: the National Identity Management Commission and Joint Admission Matriculation Board, on the use of selected technology for ethnicity recognition. The study was conducted using the quantitative method. Data were collected from the staff of selected agencies. Fingerprint, facial, and iris biometrics were selected for this experiment. Using the modified Technology Acceptance Model framework, results from the experiment showed the perception of staff on selected technologies in ethnicity recognition. Results show that technology enhanced job effectiveness, the majority of respondents agreed that fingerprint and facial biometrics would improve ethnicity detection; hence they were a popular choice for ethnicity recognition. The study showed that determining ethnicity from iris biometric would be difficult as the majority do not see the iris as a means of identifying a particular ethnic group. Five different hypotheses were tested to determine compatibility, ease of use, and attitude towards use (ATU) of the selected technology. Results showed that selected biometrics can effectively determine an ethnic group with a significance value at 0.000.
Evaluation of SRGAN Algorithm for Superresolution of Satellite Imagery on Different Sensors

Abstract. In recent years, deep learning has quickly evolved to be the go-to solution for any kind of analysis of non-linear data. One such use has been that of Generative Adversarial Networks (GAN) in the field of Computer Vision. GAN models have a variety of applications for image processing, specifically, super-resolution of images. A lot of work has been done to enhance or upscale generic RGB imagery such as the ones taken from a mobile or digital camera. However, in the field of remote sensing, it presents challenges like preserving the spatial resolution of the sensor, which is affected by a wider pixel value range and relation of a pixel to ground sampling distance (GSD). From data preparation to enhancing a complete set of tiles at scale, the upsampling/downsampling requires the ratio of number pixels to the actual area in geography to be preserved. SRGAN model has been proven to be effective for interpolating the pixels based on context. However, it was observed that the same algorithm with or without parameter tuning behaves differently based on the sensor source and target resolution. We evaluate the performance of the model from 10m to 2.5m and 2.4m to 0.6m resolution. The comparison will enable better decision making when using the enhanced images for LULC classification, segmentation, and object detection.
SI/IT Strategic Planning Using Ward and Peppard Method on Secretariat of Salatiga City Regional People's Representative Council

Secretariat of Salatiga City Regional People's Representative Council is a regional apparatus that has the task of assisting the implementation of the functions and duties of the Regional Government. Secretariat of Salatiga City Regional People's Representative Council has also played an active role in implementing information systems in its business processes, but the use of existing information systems has not been maximized. Its cause performance has not run effectively and efficiently. Therefore, it is necessary to plan a good information system strategic planning. In this research using the Ward and Peppard method starting from conducting a SWOT analysis to find out the weaknesses and strengths of the organization eventually it can face threats from outside the organization, Value Chain Analysis, and business strategy recommendations. This research will produce an application portfolio in the form of a McFarlan Strategic Grid, which will describe the information system needs that will become an information system strategic planning that will be implemented within 3 years.
Multi-criteria multi-stage game optimization

The article presents a mathematical model of a multi-stage game of the process of safe control of a transport object in possible collision situations with other encountered objects, containing a description of state variables, state and control constraints, and sets of permissible object strategies. Multi-criteria optimization tasks were formulated in the form of positional and matrix games under the conditions of playing non-cooperative and cooperative control as well as non-game optimal control. The multi-criteria control algorithms corresponding to these tasks were computer simulated in Matlab / Simulink on the example of a real situation.
Processing Structures for Computer Simulation of Human Movement

Based on the dynamics, structure and properties of imbedded multi-stack systems, certain functions of the central nervous system (CNS) can be modeled and computer programmed. These models imitate the natural system at some level and can be integrated in future more comprehensive models of the CNS in studies of human movement. Several CNS functions are discussed here. The involvement of the reticular formation (RF) in identifying objects and their properties by touch is addressed. The vision system is discussed in sensing and storage of planar images. The creation of periodic motions for dance or sport is modeled. Mental processing in is the writing or drawing of images is formulated.&#x0D; &#x0D; The touch problem addressed here is the mechanisms to explore, probe, grope and identify an object that is not visible. The object of enquiry may be part of another object, lie under another object or be part of a bigger thing. It may also be involved in the early dynamics of touch in the new-born where the vision dynamics have not yet fully developed.&#x0D; &#x0D; How the central nervous system (CNS) may pursue these tasks is one objective. One elementary attempt is to develop larger structures that can handle CNS functions, how thoughts are formulated, expanded, summarized or abandoned. Multiple Stack System seem to be convenient for handling information flowing from the sensory channels to the cerebrum and the cerebellum. By considering specific tasks, the paper focuses on several functions, connections, and structures that are involved in movement.
Zung, Imundo, and Pan (2022) How do college students use digital flashcards during self-regulated learning?

Over the past two decades, digital flashcards—that is, computer programmes, smartphone apps, and online services that mimic, and potentially improve upon, the capabilities of traditional paper flashcards—have grown in variety and popularity. Many digital flashcard platforms allow learners to make or use flashcards from a variety of sources and customise the way in which flashcards are used. Yet relatively little is known about why and how students actually use digital flashcards during self-regulated learning, and whether such uses are supported by research from the science of learning. To address these questions, we conducted a large survey of undergraduate students (n = 901) at a major U.S. university. The survey revealed insights into the popularity, acquisition, and usage of digital flashcards, beliefs about how digital flashcards are to be used during self-regulated learning, and differences in uses of paper versus digital flashcards, all of which have implications for the optimisation of student learning. Overall, our results suggest that college students commonly use digital flashcards in a manner that only partially reflects evidence-based learning principles, and as such, the pedagogical potential of digital flashcards remains to be fully realised.
To the Question of Updating the Educational Programs of the Light and Engineering Profile at Ogarev Mordovia State University Taking into Account Professional Standards

Light and engineering education in Russia is now bringing forward new priorities, which are reflected in the updated major professional educational programs both bachelor’s and master’s degree. The modern labour market in the field of light and engineering today requires the training of highly qualified specialists who possess modern information technologies, are technically literate, who know regulatory regulations and environmental requirements.This trend is observed not only in Russia, but also noted by European researchers. In the labour market, the most in demand are engineering and technical workers with knowledge of intelligent lighting control systems, the Internet of Things technologies, with ability to implement energy saving measures, as well as specialists who are able to develop and implement lighting design projects, both indoor and outdoor. The analysis of the major professional educational programs implemented in Ogarev Mordovia State University showed that the timely release of professional standards (PS) 29.013 and 29.014 allowed us to formulate professional competencies (PC) of graduates of the lighting profile, which are relevant today and will contribute to the successful career building of young professionals. Much attention is paid to the formation and development of digital competencies of specialists in the lighting industry when updating major professional educational programs. Disciplines that allow engineering and technical specialists to develop artistic and aesthetic skills and imaginative thinking are introduced. Students learn through a project-based approach, consider multi-variant solutions, getting acquainted with the current nomenclature and databases of lighting devices (LD) from various manufacturers. By creating projects, they not only learn to develop lighting installations (LI) in accordance with the regulatory quantitative and qualitative parameters, but also gain practical design skills, taking into account the visual light and colour comfort, style design. They learn to implement intelligent lighting control systems, design LD based on LED light sources. In order to further improve the educational process, it is necessary to involve more practitioners, to involve students in the implementation of real projects. Strengthening the interdisciplinary nature of graduate training, whose planned activities are related to the development of lighting design projects, requires the preparation of educational and methodological support.
Prediction of Industrial Network Security Situation Based on Noise Reduction Using EMD

Industrial Internet security is a prerequisite to ensure the high-quality development of the Industrial Internet. The significant way to curb Industrial Internet security accidents and prevent cyber threats proactively is effectively controlling the changes in network situations. In this paper, we propose a new prediction model based on Long Short-Term Memory (LSTM), minimum mean square variance criterion (MMSVC), and empirical mode decomposition (EMD), with the aim of effective noise reduction and high prediction accuracy. To minimize the disturbance of random noise, we firstly deleted several outliers in high-frequency and noisy Intrinsic Mode Functions (IMFs) decomposed by EMD. MMSVC performs well in identifying noisy IMFs without using thresholds. For the blank places, we refilled them by a certain weight with relevant figures. After that, the LSTM model was applied to predict the denoised signal. The preliminary experimental analysis illustrated that noise reduction with the EMD method could provide a significant boost in forecasting performance.
An Effective Segmentation of Breast Cancer Using Modified U-NET

During late ten years breast cancer is a major issue for women&#x0027;s death recently and also increased the quantity of breast cancer. There is more proof that ladies in 15&#x2013;54 years of age are passed on by breast cancer. Breast cancer can&#x0027;t be forestalled because its main considerations have not been distinguished. Along these lines, a prior conclusion can expand the chance of progress. This work proposed a modified U-Net deep learning model-based breast cancer segmentation and classification. Information base of MIAS mammography pictures was utilized to arrange ordinary/unusual people and harmless/threatening cancer patients and the outputs of modified U-Net showed higher accuracy than existing.
Enhancing Performance of Low-Cost Sensors Using an Infant Care Usecase

The drive toward citizen observatories, remote monitoring and early warning systems has resulted in numerous Internet of things (IoT) innovations. However, affordability and availability of these solutions challenge their sustainability in areas where they are needed the most. While low-cost sensors address this challenge, their reliability is often questionable. In this respect, this study set out to evaluate techniques that can enhance the efficiency of low-cost sensors in a bid to identify ways of developing sustainable IoT solutions. Experiments conducted using an infant postnatal care prototype demonstrate the potential of the identified techniques. The results showed that sensor calibration, configuration, fabrication, fusion, and improvising techniques have the potential to enhance the quality of low-cost sensors. Future work in this area will scale the solution to other use cases.
A multimodal differential privacy framework based on fusion representation learning

Differential privacy mechanisms vary in modalities, and there have been many methods implementing differential privacy on unimodal data. Few studies focus on unifying them to protect multimodal data, though privacy protection of multimodal data is of great significance. In our work, we propose a multimodal differential privacy protection framework. Firstly, we use multimodal representation learning to fuse different modalities and map them to the same subspace. Then based on this representation, we use the Local Differential Privacy (LDP) mechanism to protect data. We propose two protection methods for low-dimensional and high-dimensional fusion tensors respectively. The former is based on Binary Encoding, and the latter is based on multi-dimensional Fourier Transform. To the best of our knowledge, we are the first to propose LDP-based methods for the representation learning of multimodal fusion. Experimental results demonstrate the flexibility of our framework where both approaches show efficient performance as well as high data utility.
Identifying Fake Images Through CNN Based Classification Using FIDAC

Image forgery is the tampering of digital photos. Because of the ever-evolving technology, creating fake images is no longer difficult. Many researchers have provided different solutions for the detection of image forgery. In this paper, we propose our model that uses CNN for classification after being fed with ELA preprocessed images to detect image forgery, and we also introduce our created dataset - FIDAC (Forged Images Detection And Classification), which consists of original camera-clicked images along with their tampered version. Furthermore, we conducted an experimental analysis wherein we compared our proposed CNN model with famous pre-defined models on various datasets combinations.