Reviewerly/exHarmony huggingface dataset:
Brewing Umqombothi: Technicalities of a VR Prototype Merging STEM and South African Intangible Cultural Heritage

This paper describes an interactive embodied learning-based simulation in Virtual Reality that has been designed to disseminate the Intangible Cultural Heritage behind Umqombothi, a traditional South African beer-brewing practice, by instructing both the cultural process and underlying chemistry. The VR simulation includes an authentic beer-making environment based on a South African township and an abstract chemistry environment called the “Microverse”. The paper is limited to literature grounding and details of the technical prototype game and its implicit logging system.
Insect-Inspired Spiking Neural Controllers for Adaptive Behaviors in Bio-Robots

The perception-action loop is at the basis of living beings' behavior and still represents a challenging process to be modeled, due to its complexity both at the level of the input sensory system and the output behavioral repertoire, determined by the real-life constraints imposed by the environment.
30-year history of the Korean Society of Mission Studies

The Academy of Korean Missionaries was officially established in 1992. The history of the academy can be explained by two terms: the preparation term from 1991 to 2000 and the development period from 2001 to 2022. During the preparation period, the number of academy members increased, and textbooks were published. Also, the academy was associated with the Korean Evangelical Missionary Society and other missionary organizations, especially the IAMS. During the period of development, the academy held academic conferences more than four times a year and published registered journals. For further development, Korean mission theology needs to be unified based on the biblical evangelism perspectives and clarify the fundamental missional calling.
Creating a Spring Boot Project

Building an application is hard work. There are a ton of decisions to make, and plenty of things can go wrong. Using software frameworks relieves some of these headaches.
Meta-Heuristic LQI Bio-regulator Benchmark for a Permanent Magnet DC Motor on ARM Platform

There are controllers such as the Linear Quadratic Controller plus Integral part (LQI) that have presented favorable results in critical and complex processes, however, one of the disadvantages of this controller is the parameterization of the Q and R matrices, since they are obtained based on the cost of the controller and a trial and error method. Therefore, the present study aims to optimize these parameters through meta-heuristic algorithms such as: Genetic Algorithms (GA), Bacterial Foraging Optimization (BFO) and Ant Colony Optimization (ACO). In the MATLAB/SIMULINK software, the control loop programming is performed, using the control blocks of the Waijung library and with the STM32F407 card with Advanced Risk Machine (ARM) processor, the capture, reading and processing of data from the plant containing the DC motor for control is obtained. To validate the efficiency of the controller, the Integral of the Absolute Value of the Time Weighted Error (ITAE) is used and together with the Wilcoxon statistical method, it compares the optimization methods or techniques performed in the LQI controller. Interesting and favorable results were obtained for the stability and viability of each bio-controller at the moment of applying them in the speed control of the DC motor.
Construction of Music Training Intelligent Platform based on Signal Spectrum Collaborative Analysis Algorithm

This paper briefly reviews several common distinction theories and their limitations, and goes beyond the category of music itself to analyze the difference between classical music and popular music from the spectrum characteristics of music. As a human-computer interaction interface, the signal projection graph helps analysts visually and interactively identify the number of signals and cluster signal feature data. At the same time, a novel signal flow graph is designed to simultaneously display the multi-feature time-varying characteristics of different radio signals Mode. Optimize the reverse order bit and butterfly operation in the algorithm for the collected audio signal, and then use the optimized algorithm to perform spectrum analysis on audio signals in different formats.
A Distance-based Separability Measure for Internal Cluster Validation

To evaluate clustering results is a significant part of cluster analysis. Since there are no true class labels for clustering in typical unsupervised learning, many internal cluster validity indices (CVIs), which use predicted labels and data, have been created. Without true labels, to design an effective CVI is as difficult as to create a clustering method. And it is crucial to have more CVIs because there are no universal CVIs that can be used to measure all datasets and no specific methods of selecting a proper CVI for clusters without true labels. Therefore, to apply a variety of CVIs to evaluate clustering results is necessary. In this paper, we propose a novel internal CVI – the Distance-based Separability Index (DSI), based on a data separability measure. We compared the DSI with eight internal CVIs including studies from early Dunn (1974) to most recent CVDD (2019) and an external CVI as ground truth, by using clustering results of five clustering algorithms on 12 real and 97 synthetic datasets. Results show DSI is an effective, unique, and competitive CVI to other compared CVIs. We also summarized the general process to evaluate CVIs and created the rank-difference metric for comparison of CVIs’ results.
Hijacking Art: Murals as an Interface Toward Augmented Reality

From health care to education, the affordances of augmented reality (AR) have been reported from countless experiences worldwide. While virtual environments refer to synthetic environments, AR superimposes digital 2D objects or 3D models in the physical world. This paper discusses two AR experiences with hijacking murals in two different locations in Ecuador. Using action research methodology, it reflects on the communicating affordances of AR by using Easy AR and Zapworks. Although in the spatial sense, AR represents an intermediate layer of digital objects or shapes; from a communication perspective, AR constitutes a deeper and wider hypertextual layer of information. It is argued that AR enhances other types of narratives and enriches visual artwork; it articulates new content and stories and gives the user a different and extended experience (UX).
BREVE ANALISI LINGUISTICA DI UN ARTICOLO DI CRONACA

Press language is mainly conditioned by limited space and, at the same time, the news, the information must attract the readers’ attention. Be it newspapers, magazines or television, information is taken from the surrounding reality, which is why clear and precise language should be used, able to meet its first and primary objective: informing the public. An article of the most popular Italian newspaper, Il Corriere della Sera, published in March 2004, was chosen for this analysis.
“SPATRIATO”, UN NEOLOGISMO SEMANTICO D’AUTORE

The paper analyses the Italian term spatriato, an author’s semantic neologism coined by Mario Desiati in Spatriati (Einaudi, Turin, 2021), the laureate of the latest Strega Awards, the most prestigious literary prize in Italy. The new meanings come from the corresponding term in the dialect of Puglia, which is where the author comes from. The journalistic documentation of the connotations of the term follows the writer’s interviews in online press.
XAI methods for precision medicine in medical decision support systems

Over the last couple of years, explainable artificial intelligence (XAI) has witnessed tremendous development evidenced by growing research interest in the area. This could be attributed to the increasing role of machine learning, especially deep learning. While these models are highly accurate, they lack explainability and interpretability. There has been limited application of AI systems in vital fields such as precision medicine due to the aforementioned vagueness. The aim of the study is XAI for precision medicine in medical decision support systems (MDSS). The authors outline through an organized examination of literature the application of XAI in MDSS, thus highlighting the several benefits of the use of XAI as reported in the literature such as enhanced decision confidence in precision medicine. The opportunities and challenges of explainable models in MDSS were discussed. Guidelines for the implementation of XAI in MDSS have been recommended in this study while highlighting some of the opportunities and challenges.
Security-based explainable artificial intelligence (XAI) in healthcare system

Explainable Artificial Intelligence (XAI) is one of the most advanced research areas of Artificial Intelligence (AI). To explain the deep learning (DL) model is the main objective of XAI. It deals with artificial models which are understandable to humans, including the users, developers, policymakers, etc. XAI is very important in some critical domains like security, healthcare, etc. The purpose of XAI is only to provide a clear answer to the question of how the model made its decision. The explanation is very important before any system decision-making. As an example, if a system responds to a decision, it is necessary to have inside knowledge of the model about that decision. The decision can be positive or negative, but it is more important to know the decision based on characteristics. The decision of the model should be trusted when we know the internal structure of the DL model. Generally, DL models come under the black box models. So for security purposes, it is very necessary to explain a system internally for any decision-making. Security is very crucial in healthcare as well as in any other domain. The objective of this research is to provide a decision about security based on XAI which is a big challenge. We can improve security systems based on XAI for the next level. For medical/healthcare security, when we recognize human action using transfer learning techniques, one pre-trained model is considered good for action and the same action is not good in terms of accuracy using another pre-trained model. This is called the black-box model problem, and it needs to know what is the internal mechanism of both models for the same action. Why one model considers good for action and why the same action is not very well using another model? Here need a model-specific approach of post-hoc interpretability to know the internal structure and characteristics of both models for the same action.
Digital Access Brokers: Clustering and Comparison (Part I – Locator Services)

Scholarly resources published on different platforms are not often searchable and accessible to potential scholars. Google, like other search engines, does not always retrieve free articles on a consistent basis. As a result, there is a gap between the availability and discoverability of scholarly resources. Digital Access Brokers are ground-breaking developments in this context that assemble, discover, and link to open access copies of paid articles or paywalled papers legally uploaded by authors or author-posted manuscripts, with permission from publishers. Browser extensions have emerged as an alternative retrieval assistant to help scholars locate and provide free full-text access to such content available on different publisher sites, databases, open-access journals, or institutional repositories. Unlike other search engines, these extensions provide additional value-added services (such as recommending related resources, citation information, status, type of open access license, level of access and availability of copies by indicating different colours, integration with citation management tools, article metrics) along with the paper to reduce the information overload of the readers. The objective of this paper is to provide an idea about different browser extensions such as Kopernio, Open Access Button, Unpaywall, and along with their key features that might help them in selecting the best tool for their research work.
Supplemental Material: Accretion of an early Paleozoic Alaska-type arc onto northern North China: Implications for continental growth of the Central Asian orogenic belt

&lt;p&gt;Text: Analytical Methods. Table S1: Major and trace element compositions for samples in this study. Table S2: Zircon U-Pb isotopic data for samples in this study. Table S3: Zircon Lu-Hf isotopic data for the early Paleozoic plutons in the Ganqimaodu area.&lt;/p&gt;
Viars: An Intelligent Voice Agent to Prevent Inappropriate Content Display for Restricted Users

Abstract The paper, ‘VIARS: An Intelligent Voice Agent to Prevent Inappropriate Content Display for Restricted Users‘, by Rui Kang and Pei-Luen P Rau, published in Interacting with Computers explores the use voice-activated assistants
IT and Ethics

Abstract Ron Ballard FBCS gives his personal view about what ‘no harm’ means in IT.
Websites as Supplementary Reading Materials: Challenges Faced by Vocational Students

Responding the needs of vocational students to master the English skills once they graduate from vocational college, a group of vocational student used three EFL websites, namely English Club, BBC Learn English and Learn English British Council, for three months as supplementary reading materials. There were twelve students majoring in Accounting Information System from Diploma 3 Program at one of private colleges in Surakarta. This study employed questionnaire and online artefacts to collects the data. It was found that most of the students mentioned technical challenges that they faced when accessing websites as supplementary reading materials. Easy get tired and different level of English comprehension were challenges faced by them. Furthermore, students felt that they do not need to spend much money, could download the materials and able to improve their skills by accessing the websites. It is suggested to conduct further research in the same field with wider respondents and longer period by employing pre, during and post-test before and after using the websites to find out the significant impacts for the students.
Augmented Reality-Based 3D Human Hands Tracking From Monocular True Images Using Convolutional Neural Network

Precise modeling of hand tracking from monocular camera calibration parameters using semantic cues is an active area of research for the researchers due to lack of accuracy and computational overheads. In this context, deep learning-based framework (i.e., convolutional neural network-based human hands tracking in the current camera frame) has become an active research problem. In addition, tracking based on monocular camera needs to be addressed due to updated technology such as Unity3D engine and other related augmented reality plugins. This research aims to track human hands in continuous frame by using the tracked points to draw 3D model of the hands as an overlay. In the proposed methodology, Unity3D environment was used for localizing hand object in augmented reality (AR). Later, convolutional neural network was used to detect hand palm and hand keypoints based on cropped region of interest (ROI). The proposed method achieved accuracy rate of 99.2% where single monocular true images were used for tracking. Experimental validation shows the efficiency of the proposed methodology.
Research on the construction of university smart library under5G environment

The arrival of the 5G era provides new ideas and directions for the development of university libraries. How to promote the construction of university smart libraries has become a key concern. Based on this, based on the characteristics of users' needs of university libraries in the 5G era, this paper discusses the technical points of the construction of university smart libraries in the 5G environment from many angles, and looks forward to the future development of university smart libraries, so as to provide reference for related work in the future.
Design of solar conversion device for self-propelled trash cleaning boat

Cleaning boats are frequently used to remove trash from offshore areas to combat sea pollution. With a small self-propelled rubbish cleaning boat as the model, this paper presents a design for converting solar energy into power for the ship's control system. Wireless signals are transmitted via Bluetooth modules and several components of the system are controlled by Arduino platforms. Solar panels can be automatically deployed using this device. In the event of harsh weather, the emergency protection system can be activated to ensure the reliability of the system's operation.
Cryptology Based on Laplace Transform of Hyperbolic Function and Matrix Decomposition Method

Cryptography is the art of coding and decoding the communication. Cryptography ensures the security of delicate information over some confidentiality breaching resources. In the recent age Cryptography has turned into a battleground of some of the world’s best mathematicians and computer scientists. A number of transforms like Sumudu transform, Laplace transform, Fourier transform, Kamal transform, Mellin transform, Jafari transform, Aboodh transform, N-transform, ELzaki transform, MAHGOUB transform are frequently used in cryptography. Cryptography is of great importance in every field of life including data transmission, data storage, E-Commerce, confidential communication such as credit card transactions and Emails. Cryptography has been a hot topic among the researchers from 4000 years ago until now. In this work, a new cryptographic technique will be introduced. . In this work mathematical transformation known as Laplace transform on hyperbolic function is used for advancement of security purpose in the field of computers and telecommunications, this concept is known as Cryptography. This work includes a mathematical method known as “Laplace Transform” along with “Matrix Decomposition or Triangularization method” in cryptographic process (Encryption and Decryption). Both these mathematical concepts are used in this work to create a new cryptographic technique for the security of data and information in the world of computer. In this technique, Laplace transform on hyperbolic functions along with Matrix decomposition method will be incorporated. The Laplace Transform of hyperbolic function and Lower Triangular Matrix will be used as encryption and inverse Laplace Transformand upper triangular matrix will be used for decryption. Furthermore, the technique will also be explained with its applications.
(Invited) Optoelectronic Quantum Information Processing: An All-Group IV Integrated Platform

Achieving coherent optical photon-to-spin conversion is a long-sought-after strategy for surmounting current fundamental limits in optical schemes that hinder the long-distance distribution of entanglement. Moreover, photon-to-spin interfaces are also essential for a direct mapping of the quantum information encoded in photon flying qubits to stationary spin processors. However, the lack of scalable materials offering an efficient interaction with optical photons along with optimal spin properties remains a formidable obstacle hindering the development of these quantum technological components. With this perspective, this presentation will discuss strategies to address these challenges by leveraging the degrees of freedom offered by group IV (Si)GeSn semiconductors, namely strain and composition, to tailor the electronic structure and eventually fulfill these prerequisites. These innovative systems do not only have the potential to enable coherent photon-to-spin interfaces, but because of their compatibility with the semiconductor industry they will also offer scalability, manufacturability, and cost-effectiveness. We will show that this family of semiconductors provide an additional flexibility to control the charge carrier states and achieve a selective confinement of holes. The latter benefit from a quiet quantum environment that has been at the core of increasingly reliable quantum processors and memories. However, most if not all available experimental studies of two-dimensional gas systems have been thus far focused on heavy-hole (HH) states. This is attributed to the nature of the heterostructures currently available (e.g, Ge/SiGe, InGaAs/GaAs), where compressive strain lifts the valence band degeneracy and leaves HH states energetically well above the light-hole (LH) states. We will demonstrate that tensile strained Ge/GeSn quantum wells alleviate these limitations and allow to selectively confine LH provided the strain is higher than 1%. This requires strain relaxed, high Sn content GeSn buffer layers to be used to grow Ge quantum wells with LH ground state, high g-factor anisotropy, and a tunable splitting of the hole subbands. The optical and electronic properties of these low-dimensional systems will be described and discussed. Spin injection and coherent control will also be addressed. Additionally, qubit designs exploiting the ability to engineer LH states and the Ge large spin-orbit coupling allowing fast all-electrical spin-manipulation schemes will also be presented and discussed.
Analytical Validation of Surface-Enhanced Raman Scattering Paper Lateral Flow Assays for Detection of Traumatic Brain Injury Biomarker

Traumatic brain injury (TBI) can cause brain function loss injury-related deaths. Early TBI management is significant, but requires a timely, accurate, and sensitive assessment solution. However, current radiation- or imaging-based TBI diagnostic technologies are either less effective or time-consuming and expensive. Here, a surface-enhanced Raman scattering paper-based lateral flow strip (SERS-PLFS) is developed for rapid detection of neuron-specific enolase (NSE), a TBI biomarker in clinical blood plasma samples. The SERS-PLFS device is validated analytically with clinical human blood plasma samples. The performance metrics including accuracy, precision, reportable range, lower limit of quantification and upper limit of quantification, recovery, uncertainty, selectivity, robustness, are assessed using statistical methods. The validation results show that the SERS-PLFS is reliable in measuring the NSE levels in TBI patients.
How Emerging Sensor Technologies Contribute to Artificial Intelligence; Machine Learning and Deep Learning Data Acquisition Techniques

Computers Munge on Data and Without data to munge computers can as well be famished; to say literally. Deep Learning as an off-shoot of Machine Learning which itself is a sub-field of Artificial Intelligence relies solely on advancements in emerging computing Technologies which provide the computing capacity for running large scale data processing ; most importantly these include (i.e. but not limited to)advanced high level computing power and myriad data acquisition Sources as the ubiquitous Sensors proliferating the technology space of portable computing devices and IoT. Sensors Technology help in data collection in several be-spoke ways for these emerging computing technologies(since without data computers are famished typically and with deep learning sufficient data is basically needed)to be able to perform deep learning operations required for inferencing useful informative insights from crude-data. Today data is being generated in an unprecedented manner, as a result of the emergence of several sensor technologies; this has become the leverage for all deep learning computations and its successes today. In this Paper review i will be overviewing and discussing how these emerging sensors technology has been able to complement the computing power today to facilitate deep Learning technologies.
Veritas Advanced Infusion Packs

Biomedical Safety & Standards 52(22):p 173, December 15, 2022. | DOI: 10.1097/01.BMSAS.0000904556.72284.56