Miracle-dz/UniFAR-FTUs huggingface dataset:
Combinations of Theories and the Bernays-Schönfinkel-Ramsey Class

The Bernays-Schonfinkel-Ramsey (BSR) class of formulas is the class of formulas that, when written in prenex normal form, have an quantifier prefix and do not contain any function symbols. This class is decidable. We show here that BSR theories can furthermore be combined with another disjoint decidable theory, so that we obtain a decision procedure for quantifier-free formulas in the combination of the BSR theory and another decidable theory. The classical Nelson-Oppen combination scheme requires theories to be stably-infinite, ensuring that, if a model is found for both theories in the combination, models agree on cardinalities and a global model can be built. We show that combinations with BSR theories can be much more permissive, even though BSR theories are not always stably-infinite. We state that it is possible to describe exactly all the (finite or infinite) cardinalities of the models of a given BSR theory. For the other theory, it is thus only required to be able to decide if there exists a model of a given cardinality. With this result, it is notably possible to use some set operators, operators on relations, orders -- any operator that can be expressed by a set of BSR formulas -- together with the usual objects of SMT solvers, notably integers, reals, uninterpreted symbols, enumerated types.
Enhancing Augmented Reality Dialogue Systems with Multi-Modal Referential Information

In this paper, we present a novel approach to advancing augmented reality (AR) dialogue systems, bridging the gap between two-dimensional spaces and immersive virtual environments. We construct the “SIMMC2-Point” dataset, which transforms the original SIMMC2 dataset from virtual reality (VR) into AR environments, highlighting the additional introduced pointing modality to support understanding user's multi-modal intentions in AR. By harnessing the power of BART and CLIP models, we design the architecture of multi-modal dialogues that effectively captures spatial and attribute information. Then, a series of ablation experiments based on our designed SIMMC2-Point dataset and dialogue models underscores the significance of the pointing modality in enhancing the performance of dialogue systems across various tasks. Our work represents a crucial step forward in AR dialogue systems, facilitating seamless interactions within immersive virtual environments.
The Evolution of Cybersecurity in the Big Data Era Moving Beyond Data Protection to Data-Driven Security

The Evolution of Cybersecurity in the Big Data Era: Moving Beyond Data Protection to “Data-Driven Security” presents a pioneering approach that harnesses the potential of data analytics and machine learning in the cybersecurity domain. In this study, we introduce a novel methodology termed Data-Driven Cybersecurity Intelligence (DDCI), a fusion of sophisticated algorithms: Anomaly Detection using Isolation Forest, Clustering using K-Means, and Predictive Modeling using Random Forest. The DDCI method revolutionizes cybersecurity by offering efficient anomaly detection, precise grouping of similar instances, and accurate prediction of potential cyber threats. Cyber threats are continuously evolving and growing in sophistication. Traditional cybersecurity methods often fall short in keeping pace with these dynamic threats due to their reliance on static rules and signatures. DDCI, on the other hand, embraces adaptability, allowing it to evolve alongside emerging threats by utilizing data-driven techniques. This study demonstrates that DDCI is a pioneering approach in cybersecurity, showcasing the potential of data-driven security in the big data era. The comparison with traditional methods unequivocally establishes the superiority of DDCI, making a strong case for the adoption of data-driven security approaches to safeguard our increasingly interconnected digital world.
Classification of Autism Histogram of Oriented Gradient (HOG) Feature Extraction with Support Vector Machine (SVM) Method

This article aims to develop and validate an Autism Classification system using the Histogram of Oriented Gradient (HOG) feature extraction method and Support Vector Machine (SVM). The research problem focuses on the difficulties in identifying early signs of autism in children based on their facial features and patterns. To approach this issue, we use theoretical references from the field of artificial intelligence and digital image processing. This system is developed based on data collected through empirical research, which is then qualitatively and quantitatively analyzed using the SVM method. The results show that the system achieves an accuracy rate of 88%, precision of 86.5%, recall of 87%, f1-Score of 86.5% and f2-Score of 86 demonstrating the system's effectiveness in identifying children who may have autism. The article concludes by stating that the developed Autism Classification system shows significant potential in the early detection of autism in children. This suggests that AI-based facial analysis can be a valuable tool in helping parents and healthcare professionals identify early signs of autism. Recommendations for further research are to validate the system's performance on larger and more diverse datasets to increase its reliability and generalizability.
Dynamic Locomotion of Underwater Quadruped Robots Based on Whole-Body-Control Method

Unmanned Underwater Vehicles (UUVs) can operate without a human occupant and have widely application. However, there are still limitations in locomotion and manipulation on the seabed, which inspired to developed underwater legged robots. The major challenge of underwater legged robot is robust locomotion gait control under water disturbance environment. , in this paper we present a whole-body-control based control strategy of underwater quadruped robots to realize dynamic gaits, which can incorporate both body posture stabilization and dynamic gait execution. Simulation results show that the robot can track velocity commands accurately and maintain body posture stability.
Spotting Culprits in Epidemics: How Many and Which Ones?

Given a snapshot of a large graph, in which an infection has been spreading for some time, can we identify those nodes from which the infection started to spread? In other words, can we reliably tell who the culprits are? In this paper we answer this question affirmatively, and give an efficient method called NETSLEUTH for the well-known Susceptible-Infected virus propagation model. Essentially, we are after that set of seed nodes that best explain the given snapshot. We propose to employ the Minimum Description Length principle to identify the best set of seed nodes and virus propagation ripple, as the one by which we can most succinctly describe the infected graph. We give an highly efficient algorithm to identify likely sets of seed nodes given a snapshot. Then, given these seed nodes, we show we can optimize the virus propagation ripple in a principled way by maximizing likelihood. With all three combined, NETSLEUTH can automatically identify the correct number of seed nodes, as well as which nodes are the culprits. Experimentation on our method shows high accuracy in the detection of seed nodes, in addition to the correct automatic identification of their number. Moreover, we show NETSLEUTH scales linearly in the number of nodes of the graph.
Long-Wave and Short-Wave Infrared Image Conversion Based on Diffusion Model

Infrared images play a significant role in various applications, including ship detection, as they can display information about target objects in diverse complex environments. However, owing to the different formation mechanisms of long-wave and short-wave images in infrared imaging, the information they hold varies. In this paper, we propose a long-wave and short-wave infrared image conversion method based on a diffusion model to fully utilize the information in both long-wave and short-wave images, avoiding the issue of insufficient information when only a single long-wave or short-wave image can be obtained in certain situations. Specifically, we first propose a collaborative training strategy based on the diffusion model. By minimizing the optimal transport distance between the source and target images, the edge and structural information are preserved while smoothing the image regions. Next, we combine attention with traditional optimization algorithms, allowing the model to better focus on essential areas in the input image and achieve a smooth transition between the two image domains. Finally, we adopt the optimal transport algorithm based on the Sinkhorn to accelerate the model's execution speed. Simulation results demonstrate that the proposed method effectively improves training efficiency, outperforming baseline image generation methods in terms of SSIM and PSNR values on the selected dataset, while reducing the LPIPS value. The proposed method has promising application potential in the field of ship detection.
A provegetarian food pattern and reduction in total mortality in the Prevención con Dieta Mediterránea (PREDIMED) study.

BACKGROUND Vegetarian diets have been associated with reduced mortality. Because a pure vegetarian diet might not easily be embraced by many individuals, consuming preferentially plant-derived foods would be a more easily understood message. A provegetarian food pattern (FP) emphasizing preference for plant-derived foods might reduce all-cause mortality. OBJECTIVE The objective was to identify the association between an a priori-defined provegetarian FP and all-cause mortality. DESIGN We followed 7216 participants (57% women; mean age: 67 y) at high cardiovascular risk for a median of 4.8 y. A validated 137-item semiquantitative food-frequency questionnaire was administered at baseline and yearly thereafter. Fruit, vegetables, nuts, cereals, legumes, olive oil, and potatoes were positively weighted. Added animal fats, eggs, fish, dairy products, and meats or meat products were negatively weighted. Energy-adjusted quintiles were used to assign points to build the provegetarian FP (range: 12-60 points). Deaths were confirmed by review of medical records and the National Death Index. RESULTS There were 323 deaths during the follow-up period (76 from cardiovascular causes, 130 from cancer, 117 for noncancer, noncardiovascular causes). Higher baseline conformity with the provegetarian FP was associated with lower mortality (multivariable-adjusted HR for ≥ 40 compared with <30 points: 0.59; 95% CI: 0.40, 0.88). Similar results were found with the use of updated information on diet (RR: 0.59; 95% CI: 0.39, 0.89). CONCLUSIONS Among omnivorous subjects at high cardiovascular risk, better conformity with an FP that emphasized plant-derived foods was associated with a reduced risk of all-cause mortality. This trial was registered at www.controlled-trials.com as ISRCTN35739639.
Distinct Circular Single-Stranded DNA Viruses Exist in Different Soil Types

ABSTRACT The potential dependence of virus populations on soil types was examined by electron microscopy, and the total abundance of virus particles in four soil types was similar to that previously observed in soil samples. The four soil types examined differed in the relative abundances of four morphological groups of viruses. Machair, a unique type of coastal soil in western Scotland and Ireland, differed from the others tested in having a higher proportion of tailed bacteriophages. The other soils examined contained predominantly spherical and thin filamentous virus particles, but the Machair soil had a more even distribution of the virus types. As the first step in looking at differences in populations in detail, virus sequences from Machair and brown earth (agricultural pasture) soils were examined by metagenomic sequencing after enriching for circular Rep-encoding single-stranded DNA (ssDNA) (CRESS-DNA) virus genomes. Sequences from the family Microviridae (icosahedral viruses mainly infecting bacteria) of CRESS-DNA viruses were predominant in both soils. Phylogenetic analysis of Microviridae major coat protein sequences from the Machair viruses showed that they spanned most of the diversity of the subfamily Gokushovirinae, whose members mainly infect obligate intracellular parasites. The brown earth soil had a higher proportion of sequences that matched the morphologically similar family Circoviridae in BLAST searches. However, analysis of putative replicase proteins that were similar to those of viruses in the Circoviridae showed that they are a novel clade of Circoviridae-related CRESS-DNA viruses distinct from known Circoviridae genera. Different soils have substantially different taxonomic biodiversities even within ssDNA viruses, which may be driven by physicochemical factors.
Adversarial Training Classifier for Imbalanced and Semi-Supervised Learning

The imbalanced learning research has made great progress due to the introduction of generative adversarial networks (GANs). However, most studies focus on combining GAN models with oversampling techniques which could potentially compromise the distribution of the original training dataset. This study presents a cost-sensitive classifier based on the adversarial training framework that can not only deal with imbalanced data distribution but also utilize the unlabeled sample sets for semi-supervised learning. Extensive experiments are carried out to compare the classification performance on imbalanced datasets with the resampling and balanced ensemble method, as well as class-imbalanced semi-supervised scenarios, to demonstrate the advantages of the proposed method.
Embedded software development on top of transaction-level models

Early embedded SW development with transaction-level models has been broadly promoted to improve SoC design productivity. But the proposed APIs only provide low-level read/write operations via a TLM interconnect. SW developers have to implement platform-specific communication procedures and handshake protocols to access HW functions, which requires a deep understanding of both the HW interfaces and the TLM fabric used. In this paper, we propose our concept of hardware procedure calls (HPC) with which HW services are provided to SW processes as remote methods on top of transaction-level communication. To this end, a lightweight HPC protocol is presented, and we propose a method to generate the infrastructure for HPC communication from a straightforward input description. Our experiments show that with HPC, embedded SW development is considerably made easier, and that also the effort to create the transaction-level model itself is reduced.
Microbiota Separation and C-reactive Protein Elevation in Treatment-naïve Pediatric Granulomatous Crohn Disease

Objectives: In patients with inflammatory bowel diseases (IBDs), the presence of noncaseating mucosal granuloma is sufficient for diagnosing Crohn disease (CD) and may represent a specific immune response or microbial-host interaction. The cause of granulomas in CD is unknown and their association with the intestinal microbiota has not been addressed with high-throughput methodologies. Methods: The mucosal microbiota from 3 different pediatric centers was studied with 454 pyrosequencing of the bacterial 16S rRNA gene and the fungal small subunit (SSU) ribosomal region in transverse colonic biopsy specimens from 26 controls and 15 treatment-naïve pediatric CD cases. Mycobacterium avium subspecies paratuberculosis (MAP) was tested with real-time polymerase chain reaction. The correlation of granulomatous inflammation with C-reactive protein was expanded to 86 treatment-naïve CD cases. Results: The CD microbiota separated from controls by distance-based redundancy analysis (P = 0.035). Mucosal granulomata found in any portion of the intestinal tract associated with an augmented colonic bacterial microbiota divergence (P = 0.013). The granuloma-based microbiota separation persisted even when research center bias was eliminated (P = 0.04). Decreased Roseburia and Ruminococcus in granulomatous CD were important in this separation; however, principal coordinates analysis did not reveal partitioning of the groups. CRP levels >1 mg/dL predicted the presence of mucosal granulomata (odds ratio 28 [6–134.32]; 73% sensitivity, 91% specificity). Conclusions: Granulomatous CD associates with microbiota separation and C-reactive protein elevation in treatment-naïve children; however, overall dysbiosis in pediatric CD appears rather limited. Geographical/center bias should be accounted for in future multicenter microbiota studies.
Machine Learning for Agricultural Price Prediction: A Case of Coffee Commodity in Vietnam Market

Predicting agricultural commodity prices is crucial for farmers, governments, and related stakeholders to make important decisions about crop planning and food security. This is especially important in economies that rely greatly on agriculture, such as Vietnam and other developing countries, where the instability and inadequate predictability of agricultural commodity prices may lead to overproduction, "good season - devaluation" phenomenon, and significant economic losses. Aiming to address this issue, this paper presents the use of machine learning models to predict the price of coffee, a major agricultural commodity in Vietnam. Various machine learning techniques, namely LSTM (Long Short-Term Memory), ARIMA (Autoregressive Integrated Moving Average), SARIMA (Seasonal ARIMA), GRU (Gated Recurrent Unit), SVM (Support Vector Machine), and RF (Random Forest), are trained and evaluated using different data, including historical prices and additional data, such as fuel prices and weather data. The results of numerical experiments demonstrated that using additional data can significantly improve prediction performance.
Through a Glass Darkly: Reflections on Therapist Transformations

Psychologists are not immune to unfortunate, potentially devastating, life-changing events. The following article explores how a shared event, the loss of a child, impacted 2 different therapists and describes their individual changes in therapeutic perspectives and approaches. Vulnerabilities and lessons of resiliency are presented, with the direct impact on psychological practice discussed. The authors conclude that encountering personal struggles or challenges can have a transforming influence on therapists, including how therapists conceptualize and interact with their clients.
How Much Favorable Selection Is Left in Medicare Advantage?

The health economics literature contains two models of selection, one with endogenous plan characteristics to attract good risks and one with fixed plan characteristics; neither model contains a regulator. Medicare Advantage, a principal example of selection in the literature, is, however, subject to anti-selection regulations. Because selection causes economic inefficiency and because the historically favorable selection into Medicare Advantage plans increased government cost, the effectiveness of the anti-selection regulations is an important policy question, especially since the Medicare Advantage program has grown to comprise 30 percent of Medicare beneficiaries. Moreover, similar anti-selection regulations are being used in health insurance exchanges for those under 65. Contrary to earlier work, we show that the strengthened anti-selection regulations that Medicare introduced starting in 2004 markedly reduced government overpayment attributable to favorable selection in Medicare Advantage. At least some of the remaining selection is plausibly related to fixed plan characteristics of Traditional Medicare versus Medicare Advantage rather than changed selection strategies by Medicare Advantage plans.
Electronic Waste Recognition and Classification Using YOLOv8 for ICT Sector

This study develops an image recognition system to classify electronic waste (e-waste), especially the e-waste from information and communication technology (ICT) sector. From an organizational perspective, sustainable ICT e-waste management is very important due to environmental impact as well as data security and privacy compliance. This study utilizes the deep learning model YOLOv8 to recognize electronic wastes originated from a higher educational institution within the umbrella of computer accessories, batteries, storage devices, networking devices, and printed circuit boards. The dataset consists of 1690 images, divided into 13 classes, each containing 130 images. The training and validation process was carried out using Google Colab. The result shows that the model successfully classifies each type of e-waste, achieving mean Average Precision (mAP) of 95%. The results of this study will be further used to design smart bins for e-waste collection and planning at different organizations.
Application of Bayes Rule for Precise Analysis of Class-Imbalanced Stroke Data

The interpretability of stroke risk prediction is still lagging and it can be solved by using deep analysisofdata with the application of the Bayes rule. The prediction model has to be produced with accuracy, clarity, and comprehensibility without ambiguity, especially in machine learning and data analytics. The class imbalance problem is a common issue affecting the raw data, which means the imbalanced categorization of outcome feature value. In many fields, such as fraud detection, network intrusion detection, and medical diagnostics, this class imbalance issue is present where negative class samples outnumber positive class samples. If some datasets have class imbalance and imperfect issues, there will be no proper classification pattern. There are many resampling methods to address this class imbalance problem. However, these resampling methods can make an even distribution of classes by generating or omitting some samples, but theycannot handle imperfect data. The probabilistic analysis can lead to the identification of issues that exist in the dataset and provide direction to medical experts on dealing with the condition to avoid panic.The strength of applying the Bayes rule will provide a meaningful interpretation of stroke data. This paper investigates the Baye rule's significance in stroke data analysis.
Multitask Modeling for Reliability Analysis and Design with Partial Information

Accurate predictive modeling plays a pivotal role in enhancing system performance, mitigating failures, and optimizing maintenance strategies. However, traditional modeling techniques often face challenges when dealing with partially labeled datasets, leading to inaccuracies and unreliable results. In this study, we propose a multi-task surrogate modeling framework with conformal prediction to tackle these challenges effectively. The proposed model utilizes latent variables to recover unlabeled observations and reconstruct missing dimensions through latent variables. Moreover, our method leverages partially observed/labeled information from diverse sources, encompassing varying observed labels, to mitigate the computational cost associated with collecting and evaluating expensive high-dimensional complex systems. To ensure that the model is not misspecified, we employ conformal prediction to calibrate the Bayesian prediction interval and provide accurate uncertainty quantification for engineering systems. This step enhances the model's reliability and aids in making informed decisions. To validate the effectiveness of our proposed method, we demonstrate its application to a vehicle design problem. The results highlight its ability to accommodate the inherent intricacies of partially labeled datasets, propelling the field towards more robust and accurate uncertainty quantification.