Reviewerly/exHarmony huggingface dataset:
A METHOD FOR DIGITALLY RECONSTRUCTING PROJECTILE FRAGMENTATION PATTERNS IN GELATIN BLOCKS THAT REQUIRES MINIMAL DATA COLLECTION

The purpose of this paper is to propose a method for constructing a three-dimensional (3D) digital representation of projectile fragmentation patterns in gelatin blocks that requires minimal data collection. The data points required are the start and end points of each fragment’s wound track, as well as the track level designations (a means of organizing how the wound tracks are connected). The maximum likelihood estimates for the parameters of the wound track models are then used to determine the “best fit” fragmentation pattern. Though this paper focuses on the application to gelatin fragmentation patterns, the methodology can be readily used for any visco-elastic material.
A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach (Preprint)

<sec> <title>BACKGROUND</title> Wearable devices have limited ability to store and process such data. Currently, individual users or data aggregators are unable to monetize or contribute such data to wider analytics use cases. When combined with clinical health data, such data can improve the predictive power of data-driven analytics and can proffer many benefits to improve the quality of care. We propose and provide a marketplace mechanism to make these data available while benefiting data providers. </sec> <sec> <title>OBJECTIVE</title> We aimed to propose the concept of a decentralized marketplace for patient-generated health data that can improve provenance, data accuracy, security, and privacy. Using a proof-of-concept prototype with an interplanetary file system (IPFS) and Ethereum smart contracts, we aimed to demonstrate decentralized marketplace functionality with the blockchain. We also aimed to illustrate and demonstrate the benefits of such a marketplace. </sec> <sec> <title>METHODS</title> We used a design science research methodology to define and prototype our decentralized marketplace and used the Ethereum blockchain, solidity smart-contract programming language, the web3.js library, and node.js with the MetaMask application to prototype our system. </sec> <sec> <title>RESULTS</title> We designed and implemented a prototype of a decentralized health care marketplace catering to health data. We used an IPFS to store data, provide an encryption scheme for the data, and provide smart contracts to communicate with users on the Ethereum blockchain. We met the design goals we set out to accomplish in this study. </sec> <sec> <title>CONCLUSIONS</title> A decentralized marketplace for trading patient-generated health data can be created using smart-contract technology and IPFS-based data storage. Such a marketplace can improve quality, availability, and provenance and satisfy data privacy, access, auditability, and security needs for such data when compared with centralized systems. </sec>
A Few-shot Approach to Resume Information Extraction via Prompts

Prompt learning's fine-tune performance on text classification tasks has attracted the NLP community. This paper applies it to resume information extraction, improving existing methods for this task. We created manual templates and verbalizers tailored to resume texts and compared the performance of Masked Language Model (MLM) and Seq2Seq PLMs. Also, we enhanced the verbalizer design for Knowledgeable Prompt-tuning, contributing to prompt template design across NLP tasks. We present the Manual Knowledgeable Verbalizer (MKV), a rule for constructing verbalizers for specific applications. Our tests show that MKV rules yield more effective, robust templates and verbalizers than existing methods. Our MKV approach resolved sample imbalance, surpassing current automatic prompt methods. This study underscores the value of tailored prompt learning for resume extraction, stressing the importance of custom-designed templates and verbalizers.
Bodies and senses

Abstract Starting from the premise that our bodies are the primary and constant mediators of the music we experience, this chapter investigates how technology mediates the relationship between music and the human body. Often technologies offer a compromise: in providing new ways to experience music, they limit access to some of our senses. Radio, recordings, and streaming audio, for example, expand our access to music while removing or reducing the visual element of musical listening. Other technologies are designed to enhance our embodied experience of music, whether by providing visual elements or engaging with our sense of touch through what is known as vibrotactile feedback. Case studies explore music that is made but not seen, musical haptics, 18th- and 19th-century musical androids, Vocaloids, and musical holograms.
Music and Technology: A Very Short Introduction

Abstract This Very Short Introduction takes an expansive and inclusive approach meant to broaden and challenge traditional views of music and technology. In its most common use, “music technology” tends to evoke images of twentieth and twenty-first century electronic devices: synthesizers, recording equipment, music notation software, and the like. This volume, however, treats all tools used to create, store, reproduce, and transmit music—new or old, electronic or not—as technologies worthy of investigation. All musical instruments can be considered technologies. The modern piano, for example, is a marvel of keys, hammers, strings, pedals, dampers, and jacks; just the sound-producing mechanism, or action, on a piano has dozens of parts. In this broad view, technology in music encompasses instruments, whether acoustic, electric, or electronic; engraving and printing; sound recording and playback; broadcasting; software; and much more. This volume challenges the view that technology is unnatural, something external to music. It was sometimes said in the early twentieth century that so-called mechanical music (especially player pianos and phonographs) was a menace to “real” music; alternatively, technology can be freighted with utopian hopes and desires. Positive or negative, these views assume that technology is something that acts upon music. By contrast, this volume characterizes technology as an integral part of all musical activity and portrays traditional instruments and electronic machines as equally technological.
Enabling automated checking of information in factory planning with ontologies – a case study

Planning a manufacturing factory involves a high number of planners who deliver their domain-specific planning information. In Building Information Modeling, a central entity needs to check the information from different planning domains for coherence to each other. Especially for non-geometrical information, this is a manual, time-consuming and error-prone approach. The paper at hand therefore aimed at developing an expert system that automates the decision making in the checking process of factory planning information. On the basis of a case study, we have applied knowledge engineering methods from the Semantic Web and calculated an F1 score for validating our approach.
Design Lessons and Advice

Abstract This chapter discusses the tools and approaches available to designers of computer-embedded devices organized as five practical lessons. Lesson 1 considers the role of four common design tools as thinking tools in the design process. Lesson 2 examines the role physicality and context play in design, from wind-accelerating skyscrapers to toilet locks and coffee bars. Lesson 3 deals with the use of the physical as a tool to facilitate a human approach through a stronger appreciation of the way we interact with artefacts in our daily lives. Lesson 4 moves from the literal physical world and its role as a design tool to more abstracted uses of physicality. Finally, Lesson 5 returns to previously explored aspects of physicality in computer-embedded devices and how these can play out in the design world.
Computational Modelling and Implementation

Abstract The physical devices and interactions that we design and prototype also need to be coded and engineered, made into resilient, functional and usable products. This chapter looks at some of the special issues that arise when developing the computational side of physical–digital products. The chapter starts by looking at models which address some of these issues, in particular the continuity in time and space of physical phenomena and the way in which in many sensor-rich systems the impact on, and hopefully benefit to, people may be separated from the actions that were originally sensed. It then moves on to consider the development of physical systems, which are often distributed on small devices across space and involve raw electronics. Crucially developers need to deal with issues that are absent or less common in more purely digital systems, including dealing with detailed timing and delays and often grubby, pragmatic considerations.
Mind

Abstract This chapter looks at the brain and the less obviously physical mental abilities that we associate most with human use of digital technology: memory, reasoning, creativity. Yet these all arise from a grey sludge of 10 billion interconnected neurons, within a bone shell the size of a child’s toy ball. Different kinds of memory and thinking are encoded in the physical, electrical, and chemical activity of the brain and scanning technology allows us to chart the relationship between the two as blood surges to areas of highest activity. We have different kinds of intelligence tuned to our physical and social life as well as more classically cognitive functions, and these are connected both internally through language and imagination and also externally in our interactions in the world. Crucially, human creativity typically depends on this fluid movement between internal and external representations and reasoning.
CHARACTER RECOGNITION

Character recognition has numerous applications namely postal addresses, bank check amounts, documents, number plates, etc. Development of a recognition system is an emerging need for digitizing handwritten documents that use Tamil characters. Therefore, we created a prediction model to recognize Tamil characters, the model works based on the Deep convolutional neural network (CNN). In this project, model is sequential. It is like a derivation of LeNet-4 architecture, including a dropout layer.A dataset for few Tamil characters is created, along with the recognition model. It helps people in identifying the exact script written in document, where some may find it difficult to recognize the characters written in paper due to colloquial writing and different handwriting styles. CNN have given excellent results to old fashioned shallow networks in acknowledgement tasks. To avoid overfitting in the recognition model, accuracy is increased by using dropout layer and dataset increment method. With the help of these methods in the CNN model, test accuracy rate was increased. The modifiedCNN architecture achieved a highest test accuracy of 97.75%.
Strategies of GIS-Based Urban Country Park Planning and Ecological Restoration

Purpose. This study centers around the Wangzhanghe Country Park located in Jiaozuo City as the research object. Through analysis of the site’s evolution by GPS and its status-quo by GIS, the map of spatial distribution suitability has been illustrated. Design. Based on the major principle of achieving sustainable development of the ecosystem and the derived concept of building a diversified landscape, we have developed the overall scheme of planning and landscape layout of the country park. Specifically, the area of ecological restoration is further divided into the areas of quarry restoration, bare land restoration, slope protection, vegetation, and dam maintenance, and restorative measures are put forward accordingly. Findings. Our research findings indicate that thanks to the vegetation restoration of the side slope, the area of bare land in mountain farmland has been evidently reduced, whereas the severely damaged ash pits and green spots have also been restored. Originality: in the meantime, we have leveraged the flat space at the foot of the mountain to foster a viewing experience and provide recreational activities for citizens, so that they are able to effectively increase their access to outdoor events and improve their sense of engagement and experience in the events, thereby providing a reference for building a green, diverse, and sustainable system of ecological services for the urban country park.
Quantum interpolating ensemble: Bi-orthogonal polynomials and average entropies

The density matrix formalism is a fundamental tool in studying various problems in quantum information processing. In the space of density matrices, the most well-known measures are the Hilbert-Schmidt and Bures-Hall ensembles. In this work, the averages of quantum purity and von Neumann entropy for an ensemble that interpolates between these two major ensembles are explicitly calculated for finite-dimensional systems. The proposed interpolating ensemble is a specialization of the $\theta$-deformed Cauchy-Laguerre two-matrix model and new results for this latter ensemble are given in full generality, including the recurrence relations satisfied by their associated bi-orthogonal polynomials when $\theta$ assumes positive integer values.
Construction and Application Research of the Visual Image Obstacle Type Recognition Model Based on the Computer-Expanded Convolutional Neural Network

Due to the development of computer vision technology and image processing technology, obstacle recognition technology has been widely used in military and scientific research fields. However, most of the existing image-based recognition technologies are easily affected by environmental factors, which makes the application scenario of this system more fixed and cannot be applied in complex environments. This paper mainly focuses on the traditional obstacle detection and type recognition method recognition accuracy, reliability and universality is difficult to meet the technical requirements of intelligent vehicles and unmanned vehicles, traditional detection equipment cost is expensive, and other problems. There are many traditional obstacle detection methods, which basically start from the color, edge, and other information of the target object to do detection and recognition research, but their recognition accuracy, reliability, and universality are difficult to meet the technical requirements of intelligent vehicles and unmanned vehicles, and the detection equipment is expensive. The dilated convolutional neural network has the ability to learn autonomously, using the original image as input, without the cumbersome preprocessing process and can extract features of the target object one by one to achieve more accurate recognition. This design will be based on the expanded convolutional neural network, design an obstacle type detection and obstacle recognition application with high recognition accuracy, and good generalization, in which this paper applies the hierarchical structure of the expanded convolutional neural network weight sharing to learn the characteristics of various types of obstacles and extract the global features with characterization significance, combined with the ROI algorithm to achieve real-time obstacle detection and high accuracy type recognition. The ROI algorithm is combined to achieve real-time obstacle detection and high-precision type recognition.
Getting Ready for Teaching in the Pandemic Era: Action Research on Integrating ICT Course with Training and Online Teaching Practice

The purpose of this action study was to identify problems of pre-service English teachers within the context of the COVID-19 pandemic, initiate appropriate actions, and measure impacts. As the lack of teacher training and practice was noted as a problem, actions of providing long-term training and online teaching practice were initiated. Eighteen pre-service English teachers took an ICT course to receive training and taught students online. Their responses in regular group conferences, a final conference, and a survey questionnaire were analyzed through observing and reflecting. They perceived all topics and contents covered for the training as very useful. They also reported online teaching practice enabled them to improve various abilities such as managing Zoom classes, creating video content, managing an app, etc. Finally, they experienced difficulty, particularly in keeping students motivated, and holding online mentoring. However, working collaboratively enabled them to become more responsible. Actions initiated in this study were confirmed to be successful. Implications for the future are presented.
The main approaches to the description of planned learning outcomes in the Russian language adopted in Moscow Electronic School

The paper presents the main approaches to formulating planned learning outcomes in the Russian language for the "Planned Results" service of Moscow Electronic School. The research considers the principles of setting a SMART goal and the requirements for learning outcomes in this subject. These requirements are reflected in the new Federal state educational standards for basic general education. The ultimate aim of the study was to create a tool that can not only make the processes of goal setting and assessment of learners’ achievements more meaningful, but also draw teachers’ attention to the potential of research methods for teaching Russian. Based on the taxonomy of planned learning outcomes (reproductive, productive, and reflective) adopted in Moscow Electronic School, we suggest a classification of reflective type outcomes identified according to the nature of the activity. These are the groups united by the concepts of "systematization", "research", "creativity"). Additionally, the paper describes a possible logic of material presentation depending on the methods chosen by the teacher and the results whose achievement should become the result of studying particular content elements. We pay particular attention to the issues of developing and employing the activity-based approach to learning. This is manifested in our attempt to shift the focus from assessing the final result to assessing the process of completing the task. Drawing on the studies undertaken by V. V. Davydov, I. Ya. Lerner and their followers, we emphasize the importance of describing learning outcomes through naming the activity students perform in the classroom. This position is based on two fundamental principles of work, namely the unity of subject and meta-subject learning outcomes and the reliance on the activity-based approach. At the same time, the paper also highlights the importance of observing the principle of differentiation between teacher and student wording of the task. The approach indicates an understanding of goal setting and assessment as a common purpose of the student and the teacher.
Classification and reconstruction of spatially overlapping phase objects through diffractive networks

We present diffractive networks that can all-optically classify spatially-overlapping phase objects. Individual phase images of the overlapping objects are also reconstructed using electronic networks that process the compressed signals, all-optically synthesized by a diffractive network.
All-Optical Computation of an Arbitrary Linear Transform Using Diffractive Networks

We show that an arbitrary complex-valued linear transform can be performed all-optically using diffractive neural networks that are trained using deep learning.
The Work Engagement and Job Characteristics: Systematic Review and Guidelines for Future Avenues

This systematic review of forty articles aims to synthesise the increasing number of work done on work engagement and job characteristics by analysing the preferred sources from 2017 to 2021. The research studies were carefully chosen according to the researcher's inclusion criteria. The all-encompassing review of the literature has included an introduction,significance, and review of 'job characteristics theory, 'job demand theory', and elaborates innovative definition of 'work engagement' in the light of the literature review. The analyses were performed in terms of annual scientific production, most relevant sources, Bradford's law, source growth, tree map, and word dynamics through biblioshiny. MS Excel was used to evaluate the research studies. This systematic review will be beneficial to organisations and policymakers to plan the characteristics of jobs effectively to promote employee participation. Emphasis on the employees' work engagement and job characteristics will support apprehending the dream of a perfect workplace.
The Composition System of Pop Music Movement Based on Finite Element Differential Equations

Abstract This paper uses finite element differential equations to control the frequency of occurrence of pop music notes. This method avoids the repetition of dissonant notes in the song. The motivation for inspiration is used as a template in the system. Various musical techniques and rules are added to the composition process. The algorithm enables the musical composition to form a variety of other representations. The finite element differential equation algorithm used the 1/f noise ratio theory to verify that the frequency of note occurrence is directly related to how people feel when listening to music.
An Abnormalities Detection Model of a Machine Using Spectrogram

There are many methods for diagnosing abnormal conditions of machines. Among them we use a method using sound for detecting abnormalities of machines. Experimental data sets were collected at approximately 30 minutes intervals for 2 weeks. The collected data sets are converted into spectrogram images expressed by time, frequency and amplitude with a 5 second time step. In this paper, we propose a learning model created by combining Conv1D for image processing and LSTM for time series data processing to detect abnormal conditions of machines. The comparison test with the existing model combining CNN, Conv1D and GRU shows our method has a promising result.
Forecasting of Solar Radiation for a Cleaner Environment using Robust Machine Learning Techniques

Abstract An intensified research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve the worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. The Solar Radiation (SR) is intermittent, forecasting the solar radiation beforehand is a must. The objective of this research is to use Modern Machine Techniques for different climatic conditions to forecast SR with higher accuracy.The required dataset is collected from National Solar Radiation Database having features as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed and direction, with respect to the y-parameter Global Horizontal Irradiance GHI (W/m 2 ). The collected data is first split based on different types of climatic conditions. Each climatic model will be trained on various Machine Learning (ML) algorithms like Multiple Linear Regression(MLR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression(GBR), Lasso and Ridge Regression and Deep Learning Algorithm especially Long-short Term Memory (LSTM) using Google Colab Platform. From our analysis, LSTM has the least error approximation of 0.0040 loss at the 100th epoch and of all ML models, Gradient Boosting and RFR top high, when it comes to the Hot weather season – Gradient Boosting leads 2% than RFR and similarly for Cold weather, Autumn and monsoon climate –RFR has 1% higher accuracy than Gradient Boosting. This high accuracy model is deployed in a User Interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment and load dispatch centers for engineers to decide on setting up solar panels, for household clients and future researchers.