Reviewerly/exHarmony huggingface dataset:
Special Education Classrooms: Can Technology Use Inspired?

Purpose: This study aim to explore the inspiration of technology in conducting learning process for special education studens in classroom. Framework: It is common knowledge that education today requires teachers to be more creative in delivering learning and teaching in the classroom. Methodology: This study use qualitative approach in gathering the data. 12 teachers were interview in this studies. Findings: The result shows the rapidity of technology is now to some extent helping teachers find fresher ideas to conduct teaching in the classroom. The existence of various types of technology including gadgets, software, video applications and others are seen to be able to attract the interest of students in turn can achieve teaching objectives. However, some studies show that some teachers are less effective in the use of technology when teaching in the classroom. Implications: In addition to speeding up the process of obtaining information, the use of technology also actually helps understanding and attracts students to learning. It is hoped that the findings of this study can help future researchers to conduct further research. Value: This to some extent shows a loss to student learning. The use of technology should be utilized and used properly because space and opportunities for access to technology are now readily available.
Modern tools for distance education: information and analytical prospectus

The article explores modern tools used in distance education, with the goal of reviewing their benefits and limitations. Relevant definitions of distance education are first discussed in the paper and after, the key provisions of the social constructivism theory in the context of education are presented. Various types of distance education tools, including communication, learning management, content creation, assessment, and artificial intelligence tools are identified in the article, and appropriate provisions of the social constructivism theory that allow for the implementation of these tools are explained. Using the theory of social constructivism as a basis, the article analyses modern distance education tools based on various parameters, including interactivity with peers, interactivity with teachers, engagement, problem-solving, and decision-making. In the article the advantages and disadvantages of using these tools are also examined, their potential to enhance communication, collaboration, and personalised learning, while also acknowledging challenges such as a lack of social interaction, technical difficulties, and pedagogical issues are highlighted. Overall, the article emphasises the need for careful design and regulation to reduce risks and overcome limitations, making distance education a valuable option for both learners and institutions. Teachers and students should work together in order to manage these challenges and take advantage of the benefits provided by these modern tools.
On conditional belief functions in directed graphical models in the Dempster-Shafer theory

The primary goal is to define conditional belief functions in the Dempster-Shafer theory. We do so similarly to probability theory's notion of conditional probability tables. Conditional belief functions are necessary for constructing directed graphical belief function models in the same sense as conditional probability tables are necessary for constructing Bayesian networks. We provide examples of conditional belief functions, including those obtained by Smets' conditional embedding. Besides defining conditional belief functions, we state and prove a few basic properties of conditionals. In the belief-function literature, conditionals are defined starting from a joint belief function. Conditionals are then defined using the removal operator, an inverse of Dempster's combination operator. When such conditionals are well-defined belief functions, we show that our definition is equivalent to these definitions.
Generalized Dicke model and gauge-invariant master equations for two atoms in ultrastrongly-coupled cavity quantum electrodynamics

We study a generalization of the well-known Dicke model, using two dissimilar atoms in the regime of ultrastrongly-coupled cavity quantum electrodynamics. Our theory uses gauge-invariant master equations, which yields consistent results in either of the standard multipolar and Coulomb gauges, including system-bath interactions for open cavity systems. We first show how a second atom can be treated as a sensor atom to measure the output spectrum from a single atom in the ultrastrong-coupling regime, and compare results with the quantum regression theorem, explaining when they can be different. We then focus on the case where the second atom is also ultrastrongly coupled to the cavity, but with different parameters from those of the first atom, which introduces complex coupling effects and additional resonances and spectral features. In particular, we show multiple resonances in the cavity spectra that are visible off-resonance. We also observe clear anticrossing features, which are particularly pronounced when the second atom is tuned through its resonance.
Evaluation of Hands-Free VR Interaction Methods During a Fitts’ Task: Efficiency and Effectiveness

Currently, it is standard to use tracked handheld controllers for interaction in immersive virtual reality (VR). However, since VR interactions are becoming more natural with hand tracking, it is important to provide hands-free alternatives for selection and system control tasks. As such, this study aims to provide an exploratory evaluation of the effectiveness and efficiency of commonly used hands-free interfaces in selection and system control tasks. Nine interaction methods were evaluated while performing a Fitts’ law task with nine advanced users of VR in a within-subject experiment. We evaluated handheld controllers as a baseline, against head gaze, eye gaze, and voice commands for pointing at the targets, and dwell time and voice commands to confirm selections. We found that using eye gaze with a 500 ms dwell time proved to be the hand-free method with the highest performance, matching the handheld controllers and being preferred by users. The evaluation also showed that using a multimodal approach to selection, especially using the voice, decreases performance, but increases effectiveness. Moreover, we verified that Fitts’ law can be applied to hands-free methods, but its usage is limited when the methods have very short travel times. We then suggest selections per minute as a more robust comparative performance metric. Further studies should expand the audience and interaction tasks and focus on the confirmatory method of selection.
Remote Work Is Here to Stay! Reflecting on the Emerging Benefits of Mixed Reality Solutions in Industry

Changes experienced in the business landscape over the past years exacerbated by COVID-19 remain unprecedented, e.g., adaptability in the processes, and remote work, among others. One important change that resulted from this, was the accelerated switch from conducting on-site physical activities to more remote scenarios that continued to ensure such activities were maintained, despite the limitations and constraints felt all over the world. This paper discusses these rapid and large-scale changes in various sectors while emphasizing the role of remote work during this period. In particular, reporting the implications of using Mixed Reality (MR) technologies to recreate interpersonal interactions, i.e., support human activities during remote scenarios. Namely, mitigating a number of restrictions, while offering long-term improvements, as well as maintaining distributed team-members connected and motivated. Last, a systematization of current challenges and opportunities is described, providing a guiding list of important topics that must be addressed by the research community in order to create better, more robust solutions to improve how remote collaboration is conducted.
AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease

We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of disease progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD).We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000 and 2020. The progress notes no later than the first MCI diagnosis were used for the prediction. We first preprocessed the notes by deidentification, cleaning and splitting into sections, and then pre-trained a BERT model for AD (named AD-BERT) based on the publicly available Bio+Clinical BERT on the preprocessed notes. All sections of a patient were embedded into a vector representation by AD-BERT and then combined by global MaxPooling and a fully connected network to compute the probability of MCI-to-AD progression. For validation, we conducted a similar set of experiments on 2563 MCI patients identified at Weill Cornell Medicine (WCM) during the same timeframe.Compared with the 7 baseline models, the AD-BERT model achieved the best performance on both datasets, with Area Under receiver operating characteristic Curve (AUC) of 0.849 and F1 score of 0.440 on NMEDW dataset, and AUC of 0.883 and F1 score of 0.680 on WCM dataset.The use of EHRs for AD-related research is promising, and AD-BERT shows superior predictive performance in modeling MCI-to-AD progression prediction. Our study demonstrates the utility of pre-trained language models and clinical notes in predicting MCI-to-AD progression, which could have important implications for improving early detection and intervention for AD.
REFRAMING THE IMPACT OF INNOVATIVE LEARNING TECHNOLOGIES ON UNIVERSITY STUDENTS AND LECTURERS TO SAVE TIME AND IMPROVE LEARNING CHALLENGES &amp; OPPORTUNITIES

REFRAMING THE IMPACT OF INNOVATIVE LEARNING TECHNOLOGIES ON UNIVERSITY STUDENTS AND LECTURERS TO SAVE TIME AND IMPROVE LEARNING CHALLENGES & OPPORTUNITIES
Agent-Based Modelling of Ethereum Consensus

This paper presents a study of the Poof-of-Stake (PoW) Ethereum consensus protocol, following the recent switch from Proof-of-Work (PoS) to Proof-of-Stake within Merge upgrade. The new protocol has resulted in reduced energy consumption and a shift in economic incentives, but it has also introduced new threat sources such as chain reorganizations and balancing attacks. Using a simple and flexible agent-based model, this study employs a time-continuous simulation algorithm to analyze the evolution of the blocktree and assess the impact of initial conditions on consensus quality. The model simulates validator node behavior and the information propagation throughout the peer-to-peer network of validators to analyze the resulting blockchain structure. Key variables in the model include the topology of the peer-to-peer network and average block and attestation latencies. Metrics to evaluate consensus quality are established, and means to observe the model's responsiveness to changes in parameters are provided. The simulations reveal a phase transition in which the system switches from a consensus state to a non-consensus state, with a theoretical justification presented for this observation.
Unsupervised Deep Exemplar Colorization via Pyramid Dual Non-Local Attention

Exemplar-based colorization is a challenging task, which attempts to add colors to the target grayscale image with the aid of a reference color image, so as to keep the target semantic content while with the reference color style. In order to achieve visually plausible chromatic results, it is important to sufficiently exploit the global color style and the semantic color information of the reference color image. However, existing methods are either clumsy in exploiting the semantic color information, or lack of the dedicated fusion mechanism to decorate the target grayscale image with the reference semantic color information. Besides, these methods usually use a single-stage encoder-decoder architecture, which results in the loss of spatial details. To remedy these problems, we propose an effective exemplar colorization strategy based on pyramid dual non-local attention network to exploit the long-range dependency as well as multi-scale correlation. Specifically, two symmetrical branches of pyramid non-local attention block are tailored to achieve alignments from the target feature to the reference feature and from the reference feature to the target feature respectively. The bidirectional non-local fusion strategy is further applied to get a sufficient fusion feature that achieves full semantic consistency between multi-modal information. To train the network, we propose an unsupervised learning manner, which employs the hybrid supervision including the pseudo paired supervision from the reference color images and unpaired supervision from both the target grayscale and reference color images. Extensive experimental results are provided to demonstrate that our method achieves better photo-realistic colorization performance than the state-of-the-art methods. The source code is available at https://github.com/wd1511/PDNLA-Net.
Research on Map partitioning and Preprocessing Algorithms for Global Path Planning

Indoor mobile robots utilize SLAM technology to acquire indoor maps and rely on these maps to accomplish navigation tasks. In this paper, a new indoor partitioning algorithm is proposed to process indoor maps and enable it to perform in-door map partitioning tasks. Compared with the neural network partitioning algorithm, which requires large scale map data set, and the bullock cell decomposition method, which is susceptible to obstacles. The algorithm proposed in the article has the advantages of not requiring datasets to pretrain the algorithm and the indoor partitioning effect is less likely to be affected by obstacles compared to the boustrophedon cellular decomposition. Subsequently, a global path planning preprocessing algorithm based on indoor map partitioning is proposed in this paper. The essence of the algorithm is to reduce the computational effort of the global path planning algorithm by using the elimination of non-relevant regions of the indoor map. In this paper, the sampling-based global path planning algorithms RRT and PRM, and the graph search-based global path planning algorithms A* and Dijkstra are selected for testing. Finally, the average time consumed by the six groups of preprocessing algorithms based on indoor partitioning is 9. 2333ms, taking the hospital scene graph as an example. In the comparison of the three sets of global path planning tasks, the global path planning task using preprocessing showed a significant reduction in computation time compared to the global path planning task without preprocessing. the computation time of the A* algorithm was reduced by 15.69%, the computation time of the Dijkstra algorithm was reduced by 37.6%, the computation time of the RRT algorithm was reduced by 44.21%, and the computation time of the PRM algorithm was reduced by 61.28%.
Skeleton-CutMix: Mixing Up Skeleton With Probabilistic Bone Exchange for Supervised Domain Adaptation

We present Skeleton-CutMix, a simple and effective skeleton augmentation framework for supervised domain adaptation and show its advantage in skeleton-based action recognition tasks. Existing approaches usually perform domain adaptation for action recognition with elaborate loss functions that aim to achieve domain alignment. However, they fail to capture the intrinsic characteristics of skeleton representation. Benefiting from the well-defined correspondence between bones of a pair of skeletons, we instead mitigate domain shift by fabricating skeleton data in a mixed domain, which mixes up bones from the source domain and the target domain. The fabricated skeletons in the mixed domain can be used to augment training data and train a more general and robust model for action recognition. Specifically, we hallucinate new skeletons by using pairs of skeletons from the source and target domains; a new skeleton is generated by exchanging some bones from the skeleton in the source domain with corresponding bones from the skeleton in the target domain, which resembles a cut-and-mix operation. When exchanging bones from different domains, we introduce a class-specific bone sampling strategy so that bones that are more important for an action class are exchanged with higher probability when generating augmentation samples for that class. We show experimentally that the simple bone exchange strategy for augmentation is efficient and effective and that distinctive motion features are preserved while mixing both action and style across domains. We validate our method in cross-dataset and cross-age settings on NTU-60 and ETRI-Activity3D datasets with an average gain of over 3% in terms of action recognition accuracy, and demonstrate its superior performance over previous domain adaptation approaches as well as other skeleton augmentation strategies.
Investigation of the Impact of Soot on the Efficiency of Solar Panels using a Smart Intelligent Monitoring System

Globally there has been an increase in the rate of acceptance of solar Photovoltaic (PV) system because it is a source of green energy and contributes greatly to the reduction of global warming. This notwithstanding, there are environmental factors that affect the efficiency of the solar PV systems in different parts of the world. Rivers state in southern part of Nigeria is facing the challenges of air pollution and very high level of sooth production due to the proliferation of illegal refineries. The presence of soot has had great negative impacts on the environment. There has been an increase in the rate of maintenance of solar PV systems. This work is on determining the impact of soot on solar panels with the goal of increasing the efficiency of power generated in rivers state Nigeria using smart intelligent monitoring system which involved using (Internet of Things) IOT edge to cloud analytics. The work comprises of analysis of data collected at the cloud level under a soot free condition and a soot polluted condition respectively to determine the effect of soot on the panel temperature of the solar photovoltaic system. A comparison of both shows that the temperature of the solar panel under soot condition is higher than that under a soot free condition. The presence of soot reduces the output of solar panels by 27.8%. So PV system mounted in such environment will require more frequent maintenance than its counterparts mounted in locations where there is no soot condition.
Direct and indirect relationships between disordered eating, sociocultural factors, satisfaction of basic psychological needs and controlled regulation of exercise in university students

Objective: The current study aims to investigate, which of the factors are directly and indirectly associated with disordered eating in a non-clinical population of university students in Slovakia.Methods: 302 university students (52.3% female, M age=21) filled in a questionnaire assessing the perceived pressure to be thin, the internalization of appearance ideals, disordered eating, satisfaction of basic psychological needs and the regulation of exercise.The hypothesized model was tested with a parallel multiple mediation analysis using PROCESS macro.Results: The pressure to be thin from partner, the internalization of appearance ideals and the controlled regulation of exercise were directly associated with disordered eating.The pressure to be thin from media was indirectly associated with disordered eating through the internalization of appearance ideals.The pressure to be thin from partner and from family as well as satisfaction of the need for competence were indirectly associated with disordered eating through the controlled regulation of exercise.Conclusions: The results of the current study support the focus of the existing eating disorder interventions at decreasing the internalization of sociocultural appearance ideals.The results suggest the usefulness of incorporating the promotion of autonomous regulation of behaviour, especially with regard to exercising, within the prevention and intervention field.
Harmonics elimination in phase-shifting fringe projection profilometry by use of a non-filtering algorithm in frequency domain

In phase-shifting fringe projection profilometry, fringe harmonics caused by device nonlinearities as well as other factors may badly ruin the measurement results. Generally, the used phase-shifting algorithm enables restraint of effects of harmonics below a certain order depending on the number of phase shifts. When reducing the number of phase shifts for efficiency, high order harmonics will affect the phase-measuring results because of aliasing caused by insufficient sampling rate. To overcome this issue, this paper suggests a non-filtering technique operating in frequency domain, that enables improvement of measurement accuracy by eliminating effects of high order harmonics. With this technique, the phase-shifting algorithm is restated as a process of retrieving the fundamental complex fringes from the phase-shifted fringe patterns. Implementing a Fourier transform to this calculated complex fringe pattern, the actual fundamental signals and the aliased harmonics have their own lobes with separated peaks in the frequency domain. We reconstruct each order of the aliased harmonics by exploiting their relations with the fundamental signals and then estimate their magnitudes by using the spectral peaks. Instead of directly filtering the fringe spectrum, we subtract spectra of the harmonics from Fourier transform of the just calculated complex fringes, so that the Fourier spectrum of the fundamental fringes without harmonics is recovered through an iterative operation. Further, the phase map is measured accurately. Simulation and experimental results confirm that this proposed method can significantly suppress effects of fringe harmonics. Meanwhile, by taking advantage of non-filtering, it effectively preserves the edges and details of the measured surfaces from being blurred.