Reviewerly/exHarmony huggingface dataset:
The Relationship between Immersion and Psychophysiological Indicators

Abstract Psychophysiological indicators have garnered significant interest in the assessment of presence. However, despite this interest, the nature of the relationship between psychophysiological indicators and presence factors remains undetermined. Presence, the perceived realness of a mediated or virtual experience, is modulated by two factors: immersion and coherence. Immersion represents the extent and precision of the simulated sensory modalities, while coherence refers to the environment's ability to behave as expected by the user. To study the relationship between psychophysiological indicators and presence factors, we objectively manipulated immersion by altering three visual qualities. The visual qualities were set to values above, at, or below their functional threshold. These thresholds are defined as a perceptual boundary under which a sensory quality value should be considered functionally degraded. Sixty participants performed a driving task in a virtual environment under the aforementioned conditions, while we measured their cardiovascular and eye responses. We found that degraded immersion conditions yielded significantly different psychophysiological indicator results than the condition without degradation. However, we observed an effect of immersion degradation on our measured variables only when the visual conditions were set below the functional threshold. Manipulations of immersion below the functional threshold introduced unreasonable circumstances which modified our participants' behavior. Thus, our findings suggest a direct impact of immersion on coherence and highlight the sensitivity of psychophysiological indicators to the coherence of a virtual environment. These results have theoretical implications, as a presence concepts relationship model should include the direct impact of immersion on coherence.
Categorical Updating in a Bayesian Propensity Problem

We present three experiments using a novel problem in which participants update their estimates of propensities when faced with an uncertain new instance. We examine this using two different causal structures (common cause/common effect) and two different scenarios (agent-based/mechanical). In the first, participants must update their estimate of the propensity for two warring nations to successfully explode missiles after being told of a new explosion on the border between both nations. In the second, participants must update their estimate of the accuracy of two early warning tests for cancer when they produce conflicting reports about a patient. Across both experiments, we find two modal responses, representing around one-third of participants each. In the first, "Categorical" response, participants update propensity estimates as if they were certain about the single event, for example, certain that one of the nations was responsible for the latest explosion, or certain about which of the two tests is correct. In the second, "No change" response, participants make no update to their propensity estimates at all. Across the three experiments, the theory is developed and tested that these two responses in fact have a single representation of the problem: because the actual outcome is binary (only one of the nations could have launched the missile; the patient either has cancer or not), these participants believe it is incorrect to update propensities in a graded manner. They therefore operate on a "certainty threshold" basis, whereby, if they are certain enough about the single event, they will make the "Categorical" response, and if they are below this threshold, they will make the "No change" response. Ramifications are considered for the "categorical" response in particular, as this approach produces a positive-feedback dynamic similar to that seen in the belief polarization/confirmation bias literature.
Multi-Feature Video Recommendation Based on Hypergraph Convolution for Mobile Edge Environment

With the massive growth of edge devices, how to provide users with video recommendation services in a mobile edge environment has become a research hotspot. Most traditional video recommendation methods regard the relationship between user and neighbor to be linear and ignore higher-order connectivity among users, which results in poor recommendation performance. Besides, these methods use a single feature to represent user preferences, which cannot effectively alleviate the data sparsity problem. To improve the performance of video recommendation, this article proposes a multi-feature video recommendation method based on hypergraph convolution (MVRHC). Hypergraph convolution is adopted to compute user neighborhood-level features for modeling high-order correlations among users. Final features are obtained by fusing multi-party features through attention mechanism. And video recommendation is then implemented based on the obtained features. Experimental results on two real-world datasets demonstrate that MVRHC has better performance compared with baseline methods.
A Wavelet Thresholding-Based Approach for Denoising the Color Image Affected by Gaussian Noise

Image denoising is most significant in image processing, which removes noise from noisy images and restores the original image. Images can get corrupted when downloaded or imported from cameras and mobile devices. A lot of algorithms have been proposed by researchers to improve the performance of image denoising schemes. This paper illustrates a wavelet thresholding-based denoising method for denoising the color image. A modified NeighShrink thresholding method is applied for the thresholding of wavelet coefficients. The NeighShrink process uses a sub-optimal universal threshold and identical neighboring window size in all wavelet sub-bands. However, in the proposed plan, instead of a sub-optimal universal threshold, an optimal value is determined for every wavelet coefficient in each sub-band by Stein's unbiased risk estimate (SURE). MSE and PSNR are used for the performance analysis. The results confirm that the proposed method gives better results than existing algorithms. It is noticed that most of the noise affecting in the communication channel can be modeled as Gaussian noise. As per the central limit theorem, although the random variables are not normally distributed, they can be represented as Gaussian distribution. Because of these reasons, we selected images corrupted with Gaussian noise.
Applying the Trajectories Conceptual Framework: A Case Study of an IoT Health Data Monitoring Application

We present a case study on the design and development of an IoT application, called Real-time Emergency Communication System for HAZMAT Incidents (REaCH). The REaCH application utilizes the latest IoT technologies to capture and monitor individual health data (heart rate, ECG, heat index) and provides an interactive dashboard to assist incident commanders to evaluate if a firefighter needs to be removed from the scene due to a potential threat to their health and well-being. In our study we examined the design and development of a dashboard utilizing the Trajectories Conceptual Framework (TCF) and Action Research (AR). The aim of this paper is to analyze if TCF can guide the development of a dashboard from research to industry. Our study consists of four cycles that span from the requirement gathering phase to delivering a minimal viable product. We conclude that the integration of TCF and AR provides a solid approach to evaluate the design and develop a real-time health monitoring IoT dashboard. We also propose the addition of a Developer Trajectory to address the technical aspects and help guide both the front-end and back-end developers through the various stages of the applications development using IoT technology.
A Convolution Neural Network Model to Classify Handwritten Digits from Skeletons

Offline handwritten digit recognition has remained a widespread area for researchers from many decades in domains as diverse as pattern recognition, artificial intelligence, and computer vision problems. In this work, it is proposing a convolution neural network (CNN) for classifying handwritten digits. The outline of any handwritten digit is represented by generating a skeleton of the given images. Classification of these images is accomplished with convolution neural network mechanism. Experimental results were conducted on handwritten digit database of MNIST and error rate; classification accuracy was computed. A classification rate of 98.1% and 1.9% of error rate are observed on the subsamples of handwritten digit data of MNIST.
Promote or Restrict? A Co-design Practice of a Palliative Care Information Management System in China

Palliative care is a multidisciplinary clinical practice focusing on the relief of suffering, psychosocial support, and closure near the end of life. However, unique palliative care challenges require a more agile, effective, and intelligent management method. This article explores how to design, develop, evaluate (1st test and 2nd test), and iterate a whole course information management system for palliative care (named Multidisciplinary Life Care System, MLS), under the guidance of a medical innovation frame (Eagle Model), a medical co-design methodology (MeX), usability scales (SUS and HUS), based on the practice in Linfen Community Health Service Center, Shanghai, China. The iteration by co-design was arranged between two tests, to longitudinally demonstrate the impact of it. The results show: (1) Users with different educational backgrounds significantly differ in the usability feedback after co-design (F = 5.887, p < 0.05). (2) The total usability score is negatively correlated with HUS (1st test: r = -0.664, p < 0.01) (2nd test: r = -0.454, p < 0.05). (3) Users’ perception of technical support (t = 2.918, p < 0.01) and consistency of the system (t = 2.389, p < 0.05) have been observably improved after co-design. (4) There is no statistically significant change in the total SUS score (t = 1.356, p > 0.05) or HUS score (t = 0.561, p > 0.05) after co-design.
Can Waqf be a Solution to Answer The Challenges of Globalization?

This study aims to analyze the opportunities of waqf in facing the challenges of globalization such as economic challenges that affect the level of inequality and high poverty, the challenges of education globalization that affect secondary school dropout rates. and the challenges of environmental globalization that affect the exploitation of land conversion. industry to the point of causing environmental damage. The results of this study indicate that waqf can be a solution in responding to the challenges of economic globalization by using waqf for social security programs, or a profitable investment for poverty eradication programs. And the solution to the globalization of education is to use waqf funds to support the operations of educational institutions, as well as provide facilities and scholarships for students so that they can continue to experience education without being burdened with costs. And finally, the solution in responding to the challenges of globalization is to make the status of land as a waqf so that it is not easily converted into industrial land that causes climate damage.
Consumerism and Blockchain Technology

Consumerism is an ancient concept that is capturing much attention in recent literature. The consumerism movement was developed in response to power imbalances between companies and the consumer in an increasingly marketized world. It is focused on promoting consumers' rights, market transparency, and consumer freedom of choice. Consumerism aims for market equalization, meaning well-balanced markets, in which market inequalities are reduced, providing a better functioning market environment for both companies and consumers. Being initially designed to revolutionize transactions in the financial system, blockchain is a disruptive technology with potential applications in many different areas. Blockchain is a public, immutable, and permanent ledger, which works as database in a decentralized network, using cryptographic codes. Framed by consumerism literature, this chapter explores the potential applications of blockchain technology, by focusing on key consumerism claims, including marketing ethics, the security of e-commerce transactions, and supply chain traceability and transparency.
Serious Games Reloaded

Game design is not emotion engineering. The French structuralism's methodology, story-oriented, allows the creation of a minimal structure, fractal, to be developed as IF or a game. Here, in detail, is the making-of two serious game projects. In “Instory,” the narrative is created bottom-up, ruled by the space, an epitome for other cultural heritage works; “PlatoMundi” was prepared top-down, departing from the “Myth of Er,” taking the users through a virtual quest for Justice. At the centre of this structure lies the bifurcation – a place able to support choice or decision proposals. 2D evolves into 3D space-time worlds, and new geographies already invading games. Immersion is fuelled by gadgets and AI to trick the 5 senses. New brain maps alter the relationships between body and mind, and haptic props mismatch AR, MR, and VR places and shapes. DGBL merges with CBTS, useful, but still unreliable. Games cataloguing become sophisticated taxonomies, still without gold standards to tell a good game, or a good story, from a bad one.
The Role of Blockchain in C2C E-Commerce Business Models

This chapter analyses the role and potential applications that blockchain as a technology can bring to C2C e-commerce business models. The main goal is to present a systematic analysis of potential applications of blockchain in C2C e-commerce, presenting the expected benefits and the main challenges. As a proof-of-concept, the chapter will specifically address the aspect of developing a C2C market based on blockchain, addressing the specifics of the business model, implementation aspects, and interest of potential customers. The authors propose a design of a C2C platform based on blockchain, that enables efficient and secure C2C transactions without intermediaries. The authors also present the results of a preliminary readiness study, based on the UTAUT2 model of assessing technological readiness. The results point out the importance of the effect of social influence on behavioral intention. The results aim to help business and technical developers shape their blockchain-based e-commerce business models.
Democracy and Socioeconomic Performance in Africa

Despite the wide acceptance of democracy as best form of government, coups d'etat seem to have become a never-ending phenomenon in many African countries. The relationship between democracy and socioeconomic performance is examined in this chapter. This study employs exploratory and descriptive techniques in achieving this objective. Socioeconomic performance is measured with indicators such as GDP, life expectancy, literacy rate, unemployment rate, and inflation rate. Selected countries are Sudan, Burkina Faso, Nigeria, Seychelles, Botswana, South Africa, and Rwanda. The study didn't find a clear-cut relationship between democracy and socioeconomic performance in the countries under study and so concludes that leadership might be a more important variable to consider.
A Case Study of Developing Educator Digital Competences With Serious Game Design

The chapter presents a case study on the implementation of the SADDIE method, combined with the utilization of a project management tool, in the collaborative learning process of developing an educational computer game to enhance educator-specific digital competences defined in DigCompEdu. The activities in each phase of the method are presented throught the work result of three groups of students each developing their own educational game on a selected topic from computer science. Authors also provide a detailed insight into how students utilized the selected project managament tool to improve the game development process. Furthermore, an analysis is conducted to determine how the selected project management tool enhances the acquisition of specific digital competences within each of the six competence areas defined in DigCompEdu. The results demonstrate that the project management tool significantly improves the learning process in each phase of the SADDIE method and effectively supports the acquisition of educator-specific digital competencies.
A New Scalable Deep Learning Model of Pattern Recognition for Medical Diagnosis Using Model Aggregation and Model Selection

In recent years, pattern recognition has become a research area with increasing importance using several techniques. One of the most common techniques used is deep learning. This paper presents a new deep learning model to pattern recognition for medical diagnosis. The uncovering of hidden structures is performed by feature selection, model aggregation, and model selection. The deep learning model has the ability to reach the optimal solution and create complex decision boundaries when used to look for and diagnose breast cancer. The evaluation, based on 10-fold cross-validation, showed that the proposed model, which is named BaggingSMF, yielded good results and performed better than radial basis function, bidirectional associative memory, and ELMAN neural networks. Experimental studies demonstrate the multidisciplinary applications of the model.
Argument mining from Amharic argumentative texts using machine learning approach

Argument mining is an emerging science that deals with the automatic identification and extraction of arguments along with their relation from large, unstructured data that are useful for reasoning engines and computational models. Identification and extraction of argument relation from Amharic argumentative texts is a current challenge in Amharic language text processing. There have been many efforts tried on argument relation prediction for English and other European languages. This work is aimed at the design and implementation of an argument relation prediction for Amharic language using MLP, Naïve Bayes, and SVM algorithms. The study used 815 argumentative sentences collected from politically focused sources such as Amharic newspapers, weblogs, Facebook, and other social media, to evaluate argument relation prediction. The evaluation of this experiment was conducted using discourse markers, propositional semantic similarity, and a combination of these approaches, and resulted in the highest weighted average F-scores of 68%, 84%, and 88%, respectively, using Naïve Bayes, ANN and SVM. This shows that a combination approach with a SVM classifier is preferable for an Amharic argument relation prediction task. The authors tackled the problem of argument relation prediction (a subtask of argument mining) for the Amharic language, and annotated Amharic argumentative sentences (120 argument maps), making them publicly available for future research work.
Spectral mapping theorem of an abstract non-unitary quantum walk

This paper continues the previous work (Quantum Inf. Process 11 (2019)) by two authors of the present paper about a spectral mapping property of chiral symmetric unitary operators. In physics, they treat non-unitary time-evolution operators to consider quantum walks in open systems. In this paper, we generalize the above result to include a chiral symmetric non-unitary operator whose coin operator only has two eigenvalues. As a result, the spectra of such non-unitary operators are included in the (possibly non-unit) circle and the real axis in the complex plane. We also give some examples of our abstract results, such as non-unitary quantum walks defined by Mochizuki et al. Furthermore, the main theorem applies not only to quantum walks but also to the Ihara zeta function and correlated random walks on regular graphs.
A novel lightweight deep learning fall detection system based on global-local attention and channel feature augmentation

Abstract Background and Objective: Reducing the number of falls in nursing facilities is crucial to prevent significant injury, increased costs, and emotional harm. However, current fall detection systems face a trade-off between accuracy and inference speed. This work aimed to develop a novel lightweight fall detection system that can achieve high accuracy and speed while reducing computational cost and model size. Methods: We used convolutional neural networks and the channel-wise dropout and global-local attention module to train a lightweight fall detection model on over 10,000 human fall images from various scenarios. We also applied a channel-based feature augmentation module to enhance the robustness and stability of the model. Results: The proposed model achieved a detection precision of 95.1%, a recall of 93.3%, and a mean average precision of 91.8%. It also had a significantly smaller size of 1.09 million model parameters and a lower computational cost of 0.12 gigaFLOPS than existing methods. It could handle up to 20 cameras, simultaneously with a speed higher than 30 fps. Conclusion: The proposed lightweight model demonstrated excellent performance and practicality for fall detection in real-world settings, which could reduce the working pressure on medical staff and improve nursing efficiency.
Connoisseur’s Choice: Volborthite, Milpillas, Sonora, Mexico

Click to increase image sizeClick to decrease image size ACKNOWLEDGMENTSWe especially thank Günther Neumeier (danke zehr) for finding and translating the original Volborth and Hess paper, Jeff Scovil for his excellent photographs, and William Besse for his signature location map. Reviews by Bruce Cairncross, Carl Francis, and Malcolm Southwood greatly improved the manuscript. As always, Muchismas Gracias to the miners of Milpillas who risked their livelihoods and personal safety to extract and bring us these magnificent specimens.Additional informationNotes on contributorsPeter K. M. MegawDr. Peter K. M. Megaw, a consulting editor of Rocks & Minerals and longtime exhibits chair for the Tucson Gem and Mineral Show, is an exploration geologist and mineral collector who focuses on the geology and mineralogy of Mexico.James A. McGlassonJames A. McGlasson is a semiretired exploration geologist, systematic mineral collector, and analytical mineralogist who volunteers regularly at the RRUFF Mineralogical Research Lab at the University of Arizona and is a longtime benefactor of the New Mexico Tech Mineral Museum.