Reviewerly/exHarmony huggingface dataset:
Thermotion: Design and Fabrication of Thermofluidic Composites for Animation Effects on Object Surfaces

We introduce Thermotion, a novel method using thermofluidic composites to design and display thermochromic animation effects on object surfaces. With fluidic channels embedded under the object surfaces, the composites utilize thermofluidic flows to dynamically control the surface temperature as an actuator for thermochromic paints, which enables researchers and designers for the first time to create animations not only on two and three-dimensional surfaces but also on the surface made of a few flexible everyday materials. We report the design space with six animation primitives and two modification effects, and we demonstrate the design and fabrication workflow with a customized software platform for design and simulation. A range of applications is shown leveraging the objects’ dynamic displays both visually and thermally, including dynamic artifacts, teaching aids, and ambient displays. We envision an opportunity to extend thermofluidic composites to other heat-related practices for further dynamic and programmable interactions with temperature.
Building the Metaverse

Virtual worlds are progressing toward a holistic abstraction of the metaverse. While there is abundant literature and synthesis on virtual worlds and related constructs, the linkages between above scholarly work and the “metaverse” are scarce. This research study addresses this gap by focusing on three specific research pursuits: a comprehensive definition of the metaverse that subsumes virtual world literature and looks at the metaverse as a sociotechnical stack, exploring the design elements of the metaverse, and a synthesis of future research direction associated with metaverse. For achieving the above goals, a hybrid research methodology comprising bibliometric analysis and a rigorous qualitative analysis of case studies across four major metaverse players with varied end goals was employed. The interpretive qualitative analysis was further distilled by mapping the emergent themes to the theoretical lens of affordances. This work presents a novel framework of metaverse design, establishing theoretical linkages between the sociotechnical fabric and applications of the metaverse.
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner

Graph self-supervised learning (SSL), including contrastive and generative approaches, offers great potential to address the fundamental challenge of label scarcity in real-world graph data. Among both sets of graph SSL techniques, the masked graph autoencoders (e.g., GraphMAE)—one type of generative methods—have recently produced promising results. The idea behind this is to reconstruct the node features (or structures)—that are randomly masked from the input—with the autoencoder architecture. However, the performance of masked feature reconstruction naturally relies on the discriminability of the input features and is usually vulnerable to disturbance in the features. In this paper, we present a masked self-supervised learning framework1 GraphMAE2 with the goal of overcoming this issue. The idea is to impose regularization on feature reconstruction for graph SSL. Specifically, we design the strategies of multi-view random re-mask decoding and latent representation prediction to regularize the feature reconstruction. The multi-view random re-mask decoding is to introduce randomness into reconstruction in the feature space, while the latent representation prediction is to enforce the reconstruction in the embedding space. Extensive experiments show that GraphMAE2 can consistently generate top results on various public datasets, including at least 2.45% improvements over state-of-the-art baselines on ogbn-Papers100M with 111M nodes and 1.6B edges.
Recent Advances for Quantum Neural Networks in Generative Learning

Quantum computers are next-generation devices that hold promise to perform calculations beyond the reach of classical computers. A leading method towards achieving this goal is through quantum machine learning, especially quantum generative learning. Due to the intrinsic probabilistic nature of quantum mechanics, it is reasonable to postulate that quantum generative learning models (QGLMs) may surpass their classical counterparts. As such, QGLMs are receiving growing attention from the quantum physics and computer science communities, where various QGLMs that can be efficiently implemented on near-term quantum machines with potential computational advantages are proposed. In this paper, we review the current progress of QGLMs from the perspective of machine learning. Particularly, we interpret these QGLMs, covering quantum circuit Born machines, quantum generative adversarial networks, quantum Boltzmann machines, and quantum variational autoencoders, as the quantum extension of classical generative learning models. In this context, we explore their intrinsic relations and their fundamental differences. We further summarize the potential applications of QGLMs in both conventional machine learning tasks and quantum physics. Last, we discuss the challenges and further research directions for QGLMs.
Mask information-based gamma correction in fringe projection profilometry

For fringe projection profilometry (FPP), the gamma effect of the camera and projector will cause non-sinusoidal distortion of the fringe patterns, leading to periodic phase errors and ultimately affecting the reconstruction accuracy. This paper presents a gamma correction method based on mask information. Since the gamma effect will introduce higher-order harmonics into the fringe patterns, on top of projecting two sequences of phase-shifting fringe patterns having different frequencies, a mask image is projected to provide enough information to determine the coefficients of higher-order fringe harmonics using the least-squares method. The true phase is then calculated using Gaussian Newton iteration to compensate for the phase error due to the gamma effect. It does not require projecting a large number of images, and only 2 × 3 phase shift patterns and 1 mask pattern minimum are required. Simulation and experimental results demonstrate that the method can effectively correct the errors caused by the gamma effect.
A Novel Flight Dynamics Modeling Using Robust Support Vector Regression against Adversarial Attacks

&lt;div&gt;An accurate Unmanned Aerial System (UAS) Flight Dynamics Model (FDM) allows us to design its efficient controller in early development phases and to increase safety while reducing costs. Flight tests are normally conducted for a pre-established number of flight conditions, and then mathematical methods are used to obtain the FDM for the entire flight envelope. For our UAS-S4 Ehecatl, 216 local FDMs corresponding to different flight conditions were utilized to create its Local Linear Scheduled Flight Dynamics Model (LLS-FDM). The initial flight envelope data containing 216 local FDMs was further augmented using interpolation and extrapolation methodologies, thus increasing the number of trimmed local FDMs of up to 3,642. Relying on this augmented dataset, the Support Vector Machine (SVM) methodology was used as a benchmarking regression algorithm due to its excellent performance when training samples could not be separated linearly. The trained Support Vector Regression (SVR) predicted the FDM for the entire flight envelope. Although the SVR-FDM showed excellent performance, it remained vulnerable to adversarial attacks. Hence, we modified it using an adversarial retraining defense algorithm by transforming it into a Robust SVR-FDM. For validation studies, the quality of predicted UAS-S4 FDM was evaluated based on the Root Locus diagram. The closeness of predicted eigenvalues to the original eigenvalues confirmed the high accuracy of the UAS-S4 SVR-FDM. The SVR prediction accuracy was evaluated at 216 flight conditions, for different numbers of neighbors, and a variety of kernel functions were also considered. In addition, the regression performance was analyzed based on the step response of state variables in the closed-loop control architecture. The SVR-FDM provided the shortest rise time and settling time, but it failed when adversarial attacks were imposed on the SVR. The Robust-SVR-FDM step response properties showed that it could provide more accurate results than the LLS-FDM approach while protecting the controller from adversarial attacks.&lt;/div&gt;
Tipping Point Detection Using Reservoir Computing

Detection in high fidelity of tipping points, the emergence of which is often induced by invisible changes in internal structures or/and external interferences, is paramountly beneficial to understanding and predicting complex dynamical systems (CDSs). Detection approaches, which have been fruitfully developed from several perspectives (e.g., statistics, dynamics, and machine learning), have their own advantages but still encounter difficulties in the face of high-dimensional, fluctuating datasets. Here, using the reservoir computing (RC), a recently notable, resource-conserving machine learning method for reconstructing and predicting CDSs, we articulate a model-free framework to accomplish the detection only using the time series observationally recorded from the underlying unknown CDSs. Specifically, we encode the information of the CDS in consecutive time durations of finite length into the weights of the readout layer in an RC, and then we use the learned weights as the dynamical features and establish a mapping from these features to the system’s changes. Our designed framework can not only efficiently detect the changing positions of the system but also accurately predict the intensity change as the intensity information is available in the training data. We demonstrate the efficacy of our supervised framework using the dataset produced by representative physical, biological, and real-world systems, showing that our framework outperforms those traditional methods on the short-term data produced by the time-varying or/and noise-perturbed systems. We believe that our framework, on one hand, complements the major functions of the notable RC intelligent machine and, on the other hand, becomes one of the indispensable methods for deciphering complex systems.
A Comprehensive Survey on Pandemic Patient Monitoring System: Enabling Technologies, Opportunities, and Research Challenges

Sporadic occurrences of transmissible diseases have severe and long-lasting effects on humankind throughout history. These outbreaks have molded the political, economic, and social aspects of human life. Pandemics have redefined some of the basic beliefs of modern healthcare, pushing researchers and scientists to develop innovative solutions to be better equipped for future emergencies. Numerous attempts have been made to fight Covid-19-like pandemics using technologies such as the Internet of Things, wireless body area network, blockchain, and machine learning. Since the disease is highly contagious, novel research in patients' health monitoring system is essential for the constant monitoring of pandemic patients with minimal or no human intervention. With the ongoing pandemic of SARS-CoV-2, popularly known as Covid-19, innovations for monitoring of patients' vitals and storing them securely have risen more than ever. Analyzing the stored patients' data can further assist healthcare workers in their decision-making process. In this paper, we surveyed the research works on remote monitoring of pandemic patients admitted in hospitals or quarantined at home. First, an overview of pandemic patient monitoring is given followed by a brief introduction of enabling technologies i.e. Internet of Things, blockchain, and machine learning to implement the system. The reviewed works have been classified into three categories; remote monitoring of pandemic patients using IoT, blockchain-based storage or sharing platforms for patients' data, and processing/analyzing the stored patients' data using machine learning for prognosis and diagnosis. We also identified several open research issues to set directions for future research.
Flux: Liquid Types for Rust

We introduce Flux, which shows how logical refinements can work hand in glove with Rust's ownership mechanisms to yield ergonomic type-based verification of low-level pointer manipulating programs. First, we design a novel refined type system for Rust that indexes mutable locations, with pure (immutable) values that can appear in refinements, and then exploits Rust's ownership mechanisms to abstract sub-structural reasoning about locations within Rust's polymorphic type constructors, while supporting strong updates. We formalize the crucial dependency upon Rust's strong aliasing guarantees by exploiting the Stacked Borrows aliasing model to prove that "well-borrowed evaluations of well-typed programs do not get stuck". Second, we implement our type system in Flux, a plug-in to the Rust compiler that exploits the factoring of complex invariants into types and refinements to efficiently synthesize loop annotations-including complex quantified invariants describing the contents of containers-via liquid inference. Third, we evaluate Flux with a benchmark suite of vector manipulating programs and parts of a previously verified secure sandboxing library to demonstrate the advantages of refinement types over program logics as implemented in the state-of-the-art Prusti verifier. While Prusti's more expressive program logic can, in general, verify deep functional correctness specifications, for the lightweight but ubiquitous and important verification use-cases covered by our benchmarks, liquid typing makes verification ergonomic by slashing specification lines by a factor of two, verification time by an order of magnitude, and annotation overhead from up to 24% of code size (average 14%), to nothing at all.
A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation

Abstract Objective. High-resolution multi-modal magnetic resonance imaging (MRI) is crucial in clinical practice for accurate diagnosis and treatment. However, challenges such as budget constraints, potential contrast agent deposition, and image corruption often limit the acquisition of multiple sequences from a single patient. Therefore, the development of novel methods to reconstruct under-sampled images and synthesize missing sequences is crucial for clinical and research applications. Approach . In this paper, we propose a unified hybrid framework called SIFormer, which utilizes any available low-resolution MRI contrast configurations to complete super-resolution (SR) of poor-quality MR images and impute missing sequences simultaneously in one forward process. SIFormer consists of a hybrid generator and a convolution-based discriminator. The generator incorporates two key blocks. First, the dual branch attention block combines the long-range dependency building capability of the transformer with the high-frequency local information capture capability of the convolutional neural network in a channel-wise split manner. Second, we introduce a learnable gating adaptation multi-layer perception in the feed-forward block to optimize information transmission efficiently. Main results . Comparative evaluations against six state-of-the-art methods demonstrate that SIFormer achieves enhanced quantitative performance and produces more visually pleasing results for image SR and synthesis tasks across multiple datasets. Significance . Extensive experiments conducted on multi-center multi-contrast MRI datasets, including both healthy individuals and brain tumor patients, highlight the potential of our proposed method to serve as a valuable supplement to MRI sequence acquisition in clinical and research settings.
Educational Platforms: Digital Tools for the teaching-learning process in Education

Educational platforms are digital tools that have revolutionized the teaching-learning process in education. Through a bibliographic-type methodology, relevant information has been compiled from various authors and experts in the field. These platforms offer access and flexibility, allowing students to learn anytime, anywhere, adapting to their individual needs. They facilitate interaction and collaboration between students and teachers, through discussion forums, online chats and shared spaces. In addition, they focus on personalization and adaptability, offering personalized learning paths and adaptive content. Assessment and progress tracking are also key aspects of educational platforms, providing tools for taking online assessments and generating grade reports. Lastly, they offer a wide range of educational resources and content, such as texts, videos and interactive activities, enriching the teaching-learning process. Finally, educational platforms, supported by a bibliographic methodology, have transformed education by providing flexible access, fostering interaction and collaboration, enabling personalization of learning, facilitating assessment and monitoring of progress, and offering a variety of educational resources. These digital tools have significantly improved the educational process, adapting to the needs of students in the digital age.
Optimizing quantum circuits with Riemannian gradient flow

Variational quantum algorithms are a promising class of algorithms that can be performed on currently available quantum computers. In most settings, the free parameters of a variational circuit are optimized using a classical optimizer that updates parameters in Euclidean geometry. Since quantum circuits are elements of the special unitary group, we can consider an alternative optimization perspective that depends on the structure of this group. In this work, we investigate a Riemannian optimization scheme over the special unitary group and we discuss its implementation on a quantum computer. We illustrate that the resulting Riemannian gradient-flow algorithm has favorable optimization properties for deep circuits and that an approximate version of this algorithm can be performed on near-term hardware. We highlight the connections of our work with previously proposed heuristics like ADAPT-VQE and show that they can be understood as variants of our algorithm.
The Impact of E-Service Quality on Institutional Excellence Within abu Dhabi Municipality in UAE

Purpose: The study aims to empirically examine the influence of different organisational practices on institutional excellence in municipality of Abu Dhabi. E-service quality is examined as mediator and quality of polices is examined as a moderator. Theoretical Framework: Activity theory support the assumption that the excellence is a result of the integrated system that encompass the people through their activities and interaction. The proposed conceptual framework includes three dependent variables; employee motivation, internal knowledge sharing, and collaborative e-culture. The three variables hypothesised to influence E-service quality as a mediator and institutional excellence as the dependent variable. Besides, quality of polices is hypothesized to moderate the relationship between the mediator and the dependent variable. Design/Methodology/Approach: The quantitative research methodology was considered from the positivist philosophical perspective. With the help of the survey questionnaire, data was collected from 335 employees in Abu Dhabi municipality who represent the population of 2500. The data analysis used the PLS-SEM techniques by using the software package SmartPLS ver. 3.0, Findings: The results show that employee motivation, internal knowledge sharing, and collaborative culture are significant antecedents of e-service quality even though e-service quality fails to significantly predict institutional excellence. Also, company quality policy enforcement does not moderate the effect of e-service quality on excellence. Research Practical and Social Implications: The results are useful for the decision makers in Abu Dhabi municipal and other organisations in the UAE because it shows that the proposed practices have essential impact on the organisational excellence. Originality/Value: The study is the first study to be conducted in Abu Dhabi municipality. Besides, the examination of E-services quality as a mediator towards institutional excellence is unique add on to the academic knowledge.
Arabic ChatGPT Tweets Classification Using RoBERTa and BERT Ensemble Model

ChatGPT OpenAI, a large-language chatbot model, has gained a lot of attention due to its popularity and impressive performance in many natural language processing tasks. ChatGPT produces superior answers to a wide range of real-world human questions and generates human-like text. The new OpenAI ChatGPT technology may have some strengths and weaknesses at this early stage. Users have reported early opinions about the ChatGPT features, and their feedback is essential to recognize and fix its shortcomings and issues. This study uses the ChatGPT tweets Arabic dataset to automatically find user opinions and sentiments about ChatGPT technology. The dataset is preprocessed and labeled using the TextBlob Arabic Python library into positive, negative, and neutral tweets. Despite extensive works for the English language, languages like Arabic are less studied regarding tweet analysis. Existing literature about Arabic tweet sentiment analysis has mainly focused on machine learning and deep learning models. We collected a total of 27,780 unstructured tweets from Twitter using the Tweepy SNscrape Python library using various hash-tags such as # Chat-GPT, #OpenAI, #Chatbot, Chat-GPT3, and so on. To enhance the model’s performance and reduce computational complexity, unstructured tweets are converted into structured and normalized forms. Tweets contain missing values, URL and HTML tags, stop words, punctuation, diacritics, elongations, and numeric values that have no impact on the model performance; hence, these increase the computational cost. So, these steps are removed with the help of Python preprocessing libraries to enhance text quality and consistency. This study adopts Transformer-based models such as RoBERTa, XLNet, and DistilBERT that automatically classify the tweets. Additionally, a hybrid transformer-based model is proposed to obtain better results. The proposed hybrid model is developed by combining the hidden outputs of the RoBERTA and BERT models using a concatenation layer, then adding dense layers with “Relu” activation employed as a hidden layer to create non-linearity and a “softmax” activation function for multiclass classification. They differ from existing state-of-the-art models due to the enhanced capabilities of both models in text classification. Hybrid models combine the different models to make accurate predictions and reduce bias and enhanced the overall results, while state-of-the-art models are incapable of making accurate predictions. Experiments show that the proposed hybrid model achieves 96.02% accuracy, 100% precision on negative tweets, and 99% recall for neutral tweets. The performance of the proposed model is far better than existing state-of-the-art models.