Reviewerly/exHarmony huggingface dataset:
Layer-refined Graph Convolutional Networks for Recommendation

Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological structure of the user-item interaction graph. However, these GCN-based recommendation models not only suffer from over-smoothing when stacking too many layers but also bear performance degeneration resulting from the existence of noise in user-item interactions. In this paper, we first identify a recommendation dilemma of over-smoothing and solution collapsing in current GCN-based models. Specifically, these models usually aggregate all layer embeddings for node updating and achieve their best recommendation performance within a few layers because of over-smoothing. Conversely, if we place learnable weights on layer embeddings for node updating, the weight space will always collapse to a fixed point, at which the weighting of the ego layer almost holds all. We propose a layer-refined GCN model, dubbed LayerGCN, that refines layer representations during information propagation and node updating of GCN. Moreover, previous GCN-based recommendation models aggregate all incoming information from neighbors without distinguishing the noise nodes, which deteriorates the recommendation performance. Our model further prunes the edges of the user-item interaction graph following a degree-sensitive probability instead of the uniform distribution. Experimental results show that the proposed model outperforms the state-of-the-art models significantly on four public datasets with fast training convergence. The implementation code of the proposed method is available at https://github.com/enoche/ImRec.
Saguaro: An Edge Computing-Enabled Hierarchical Permissioned Blockchain

We present Saguaro, a permissioned blockchain system designed specifically for edge computing networks. Saguaro leverages the hierarchical structure of edge computing networks to reduce the overhead of wide-area communication by presenting several techniques. First, Saguaro proposes coordinator-based and optimistic protocols to process cross-domain transactions with low latency where the lowest common ancestor of the involved domains coordinates the protocol or detects inconsistency. Second, data are collected over hierarchy enabling higher-level domains to aggregate their sub-domain data. Finally, transactions initiated by mobile edge devices are processed without relying on high-level fog and cloud servers. Our experimental results across a wide range of workloads demonstrate the scalability of Saguaro in supporting a range of cross-domain and mobile transactions.
Targeted Data Extraction and Deepfake Detection with Blockchain Technology

By recording instances of significant forensic relevance, smartphones, which are becoming increasingly crucial for documenting ordinary life events, can produce pieces of evidence in court. Due to privacy or other issues, not everyone is open to having all the data on their phone collected and analyzed. In addition, Law Enforcement Organizations need a lot of memory to keep the information taken from a witness’s phone. Deepfakes which are purposefully utilized as a source of disinformation, manipulation, harassment, and persuasion in court, present another significant problem for law enforcement organizations. Recently, the introduction of blockchain has altered the way we conduct business. Decentralized Applications (Dapps) may be a fantastic way to verify the accuracy of the data, stop the spread of false information, extract specific data with precision, and offer a framework for sharing that takes into account privacy and memory issues. This article outlines the creation of a Dapp that provides users with a secure conduit through distributing evidence that has been verified. By utilizing machine learning (ML) classifiers, this platform not only distinguishes between altered and original material before allowing it, but also uses user-uploaded media to retrain its models to increase prediction accuracy and offer complete transparency. The end outcome of this activity can maintain a clear record (timestamp) of the occurrence, submitted proof, and helpful metadata with the aid of the blockchains’ consensus notion.
MSDT: Masked Language Model Scoring Defense in Text Domain

Pre-trained language models allowed us to process downstream tasks with the help of fine-tuning, which aids the model to achieve fairly high accuracy in various Natural Language Processing (NLP) tasks. Such easily-downloaded language models from various websites empowered the public users as well as some major institutions to give a momentum to their real-life application. However, it was recently proven that models become extremely vulnerable when they are backdoor attacked with trigger-inserted poisoned datasets by malicious users. The attackers then redistribute the victim models to the public to attract other users to use them, where the models tend to misclassify when certain triggers are detected within the training sample. In this paper, we will introduce a novel improved textual backdoor defense method, named MSDT, that outperforms the current existing defensive algorithms in specific datasets. The experimental results illustrate that our method can be effective and constructive in terms of defending against backdoor attack in text domain.
Quantum reinforcement learning in the presence of thermal dissipation

A study of the effect of thermal dissipation on quantum reinforcement learning is performed. For this purpose, a nondissipative quantum reinforcement learning protocol is adapted to the presence of thermal dissipation. Analytical calculations as well as numerical simulations are carried out, obtaining evidence that dissipation does not significantly degrade the performance of the quantum reinforcement learning protocol for sufficiently low temperatures, in some cases even being beneficial. Quantum reinforcement learning under realistic experimental conditions of thermal dissipation opens an avenue for the realization of quantum agents to be able to interact with a changing environment, as well as adapt to it, with many plausible applications inside quantum technologies and machine learning.
Towards Attention–aware Foveated Rendering

Foveated graphics is a promising approach to solving the bandwidth challenges of immersive virtual and augmented reality displays by exploiting the falloff in spatial acuity in the periphery of the visual field. However, the perceptual models used in these applications neglect the effects of higherlevel cognitive processing, namely the allocation of visual attention, and are thus overestimating sensitivity in the periphery in many scenarios. Here, we introduce the first attention-aware model of contrast sensitivity. We conduct user studies to measure contrast sensitivity under different attention distributions and show that sensitivity in the periphery drops significantly when the user is required to allocate attention to the fovea. We motivate the development of future foveation models with another user study and demonstrate that tolerance for foveation in the periphery is significantly higher when the user is concentrating on a task in the fovea. Analysis of our model predicts significant bandwidth savings over those afforded by current models. As such, our work forms the foundation for attention-aware foveated graphics techniques.
FactorMatte: Redefining Video Matting for Re-Composition Tasks

We propose Factor Matting , an alternative formulation of the video matting problem in terms of counterfactual video synthesis that is better suited for re-composition tasks. The goal of factor matting is to separate the contents of a video into independent components, each representing a counterfactual version of the scene where the contents of other components have been removed. We show that factor matting maps well to a more general Bayesian framing of the matting problem that accounts for complex conditional interactions between layers. Based on this observation, we present a method for solving the factor matting problem that learns augmented patch-based appearance priors to produce useful decompositions even for video with complex cross-layer interactions like splashes, shadows, and reflections. Our method is trained per-video and does not require external training data or any knowledge about the 3D structure of the scene. Through extensive experiments, we show that it is able to produce useful decompositions of scenes with such complex interactions while performing competitively on classical matting tasks as well. We also demonstrate the benefits of our approach on a wide range of downstream video editing tasks. Our project website is at: https://factormatte.github.io/.
DreamFace: Progressive Generation of Animatable 3D Faces under Text Guidance

Emerging Metaverse applications demand accessible, accurate and easy-to-use tools for 3D digital human creations in order to depict different cultures and societies as if in the physical world. Recent large-scale vision-language advances pave the way for novices to conveniently customize 3D content. However, the generated CG-friendly assets still cannot represent the desired facial traits for human characteristics. In this paper, we present Dream-Face, a progressive scheme to generate personalized 3D faces under text guidance. It enables layman users to naturally customize 3D facial assets that are compatible with CG pipelines, with desired shapes, textures and fine-grained animation capabilities. From a text input to describe the facial traits, we first introduce a coarse-to-fine scheme to generate the neutral facial geometry with a unified topology. We employ a selection strategy in the CLIP embedding space to generate coarse geometry, and subsequently optimize both the detailed displacements and normals using Score Distillation Sampling (SDS) from the generic Latent Diffusion Model (LDM). Then, for neutral appearance generation, we introduce a dual-path mechanism, which combines the generic LDM with a novel texture LDM to ensure both the diversity and textural specification in the UV space. We also employ a two-stage optimization to perform SDS in both the latent and image spaces to significantly provide compact priors for fine-grained synthesis. It also enables learning the mapping from the compact latent space into physically-based textures (diffuse albedo, specular intensity, normal maps, etc.). Our generated neutral assets naturally support blendshapes-based facial animations, thanks to the unified geometric topology. We further improve the animation ability with personalized deformation characteristics. To this end, we learn the universal expression prior in a latent space with neutral asset conditioning using the cross-identity hypernetwork, we subsequently train a neural facial tracker from video input space into the pre-trained expression space for personalized fine-grained animation. Extensive qualitative and quantitative experiments validate the effectiveness and generalizability of DreamFace. Notably, DreamFace can generate realistic 3D facial assets with physically-based rendering quality and rich animation ability from video footage, even for fashion icons or exotic characters in cartoons and fiction movies.
UniTune: Text-Driven Image Editing by Fine Tuning a Diffusion Model on a Single Image

Text-driven image generation methods have shown impressive results recently, allowing casual users to generate high quality images by providing textual descriptions. However, similar capabilities for editing existing images are still out of reach. Text-driven image editing methods usually need edit masks, struggle with edits that require significant visual changes and cannot easily keep specific details of the edited portion. In this paper we make the observation that image-generation models can be converted to image-editing models simply by fine-tuning them on a single image. We also show that initializing the stochastic sampler with a noised version of the base image before the sampling and interpolating relevant details from the base image after sampling further increase the quality of the edit operation. Combining these observations, we propose UniTune, a novel image editing method. UniTune gets as input an arbitrary image and a textual edit description, and carries out the edit while maintaining high fidelity to the input image. UniTune does not require additional inputs, like masks or sketches, and can perform multiple edits on the same image without retraining. We test our method using the Imagen model in a range of different use cases. We demonstrate that it is broadly applicable and can perform a surprisingly wide range of expressive editing operations, including those requiring significant visual changes that were previously impossible.
AmpTorch: A Python package for scalable fingerprint-based neural network training on multi-element systems with integrated uncertainty quantification

Machine learning (ML) force fields use ML models to predict the energy (and forces) given a chemical system defined by a set of atomic coordinates.Fingerprint-based and graph-based ML force fields are two major categories of atomistic machine-learning models.Fingerprint-based models convert the atomic coordinates into fixed-length "feature vectors" that are used as inputs to regression models such as neural networks or kernel ridge regression, while graph-based systems convert the coordinates into a molecular graph which is directly input into a deep learning model.Fingerprint-based approaches tend to require less training data and be more interpretable, while graph-based models are typically able to handle more complex chemical systems and are more accurate in the limit of large datasets.Fingerprint-based models are widely used due to their conceptual similarity to traditional force-fields, but most existing packages for fingerprint-based neural network force fields are limited to systems with relatively few (< 5) elements and relatively small (< 10 6 ) datasets.
Virtual reality: a promising instrument to promote sail education

Sailing has gained an increasing attention among children and adolescents in China, which raised a strong need for sail courses through physical education (PE). However, challenges in teaching practice arise with rapid development of the sport. In the current study, we proposed a perspective that virtual reality (VR) technology makes high-quality sail education accessible for students. Critical analysis summarized the prominent features that enhance sail education, including immersive experience, interactive learning, the first-person view, and practice under well-controlled conditions. Further, research on VR sport training indicated successful transfer from virtual environment to real situation. Specifically, significant improvement in skill performance and tactical behaviors were identified, which was attributed to the enhanced perception-action coupling after VR training. Additionally, VR-based coding programs provide students with affordances of designing the virtual environment. The content design education promotes comprehension and application of knowledge and theories when students develop the simulated environment with a high level of presence. Therefore, VR technology is a promising instrument to meet the increasing demand on sail education. While VR enriches educational resources for a large class size, the interdisciplinary feature of VR-based sail course can attract students with different study interests and backgrounds to the class.
Prediction of measles patients using machine learning classifiers: a comparative study

Abstract Background Measles has high primary reproductive number, extremely infectious and ranked second to malaria in terms of disease burden in Ghana. Owing to the disease’s high infectious rate, making early diagnosis based on an accurate system can help limit the spread of the disease. Studies have been conducted to derive models to serve as preliminary tools for early detection. However, these derived models are based on traditional methods, which may be limited in terms of model sensitivity and prediction power. This study focuses on comparing the performance of five machine learning classification techniques with a traditional method for predicting measles patients in Ghana. The study was an analytical cross-sectional design of suspected measles cases in Ghana. Results The performance of six classifiers were compared and the random forest (RF) model demonstrated better performance among other models. The RF model achieved the highest sensitivity (0.88) specificity (0.96), ROC (0.92) and total accuracy (0.92). Conclusions Our findings showed that, despite all the six methods had good performance in classifying measles patients, the RF model outperformed all the other classifiers in terms of different criteria in prediction accuracy. Accordingly, this approach is an effective classifier for predicting measles in the early stage.
Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning

Sickle cell disease (SCD) is an inherited hematological disorder associated with high mortality rates, particularly in sub-Saharan Africa. SCD arises due to the polymerization of sickle hemoglobin, which reduces flexibility of red blood cells (RBCs), causing blood vessel occlusion and leading to severe morbidity and early mortality rates if untreated. While sickle solubility tests are available to sub-Saharan African population as a means for detecting sickle hemoglobin (HbS), the test falls short in assessing the severity of the disease and visualizing the degree of cellular deformation. Here, we propose use of holographic cytometry (HC), a high throughput, label-free imaging modality, for comprehensive morphological profiling of RBCs as a means to detect SCD. For this study, more than 2.5 million single-cell holographic images from normal and SCD patient samples were collected using the HC system. We have developed an approach for specially defining training data to improve machine learning classification. Here, we demonstrate the deep learning classifier developed using this approach can produce highly accurate classification, even on unknown patient samples.
Mobile augmented reality supporting families’ immersive collaborative learning: Learning-on-the-move for place-based geoscience sense-making

Abstract We investigated how families experienced immersion as they collaboratively made sense of geologic time and geoscience processes during a place-based, learning-on-the-move (LOTM) experience mediated by a mobile augmented reality (MAR) app. Our team developed an MAR app, Time Explorers , that focused on how rock-water interactions shaped Appalachia over millions of years. Data were collected at the Children’s Garden at the Arboretum at Penn State. Data sources were videos of app usage, point-of-view camera recordings with audio capturing family conversations, and interviews from 17 families (51 people). The analytical technique was interaction analysis, in which episodes of family sense-making were identified and developed into qualitative vignettes focused on how immersion did or did not support learning about geoscience and geologic time. We analyzed how design elements supported sensory, actional, narrative, and social immersion through photo-taking, discussion prompts, and augmented reality visualizations. Findings showed that sensory and social immersion supported sense-making conversations and observational inquiry, while narrative and actional immersion supported deep family engagement with the geoscience content. At many micro-sites of learning, families engaged in multiple immersive processes where conversations, observational inquiry, and deep engagement with the geoscience came together during LOTM. This analysis contributes to the CSCL literature on theory related to LOTM in outdoor informal settings, while also providing design conjectures in an immersive, family-centered, place-based LOTM framework.
A fuzzy interval model for assessing patient status and treatment effectiveness using blood morphology

This study explores the generalization of heterogeneous medical data for monitoring anomalies and changes over time using fuzzy intervals. The most important feature of these intervals is saving the parameter value as a membership function from the interval [0, 1]. An example illustrating this method is the blood count parameters of an oncological patient recorded for three years with a monthly frequency. Over 20 typical measurements of these features are considered, and eight with the highest variance are selected. The registration of the overall picture of changes, a synthesis of eight fuzzy intervals, allowed for observing a systematic improvement in health. This approach allows the doctor to take a holistic view of the patient's health (based on blood tests), avoiding the dilemma of which parameters are less and which are more important. The Mamdani fuzzy inference system was used to assess the patient's health status.The study presents the actual results of medical measurements, and the GitHub repository contains measurement data.
Stochastic conformal anomaly detection and resolution for air traffic control

Safety is of the utmost importance in the air traffic system. In recent years, data-driven algorithms have emerged to identify anomalous and potentially unsafe operations based on machine learning techniques. Although many algorithms have shown notable progress in anomaly detection, they have hardly considered the fact that data can be corrupted by noise and uncertainty (e.g., navigation system error) can lead to frequent misdetection and false alarms, which could disturb air traffic controllers and result in system performance degradation. Therefore, an accurate and reliable assessment of emerging safety risks that accounts for and alleviates the effect of uncertainty in data is required for safe and efficient airspace operations. To achieve this goal, this paper proposes a conformal prediction-based framework that explicitly examines uncertainty for reliable anomaly detection and learns online from new streaming data. In addition to supporting the monitoring task of air traffic controllers, the proposed method takes one step forward and provides support for the control task, by offering a resolution strategy when anomaly probability violates the predefined threshold. The proposed method is demonstrated with real air traffic data, called automatic dependent surveillance-broadcast data.
Extended reality in AEC

This article presents a survey on extended reality (XR) application to architecture, engineering, and construction (AEC). A systematic literature review (SLR) of studies found in the Scopus digital library is carried out for that purpose. The SLR comprises 983 relevant papers published between 2011 and 2022. It frames and aggregates research by the AEC field and by the usage of XR technologies. With that aim, specific taxonomies are developed and applied. Research typologies, including used approaches and contributions, are also examined. Furthermore, the limitations and challenges cited in the analyzed studies are addressed. This SLR is primarily addressed to researchers for decision-making in identifying and scoping future research activities. It is also directed to AEC practitioners as a source to determine the conditions in which XR can be used and what its implications are.