Reviewerly/exHarmony huggingface dataset:
A robust NIfTI image authentication framework to ensure reliable and safe diagnosis

Advancements in digital medical imaging technologies have significantly impacted the healthcare system. It enables the diagnosis of various diseases through the interpretation of medical images. In addition, telemedicine, including teleradiology, has been a crucial impact on remote medical consultation, especially during the COVID-19 pandemic. However, with the increasing reliance on digital medical images comes the risk of digital media attacks that can compromise the authenticity and ownership of these images. Therefore, it is crucial to develop reliable and secure methods to authenticate these images that are in NIfTI image format. The proposed method in this research involves meticulously integrating a watermark into the slice of the NIfTI image. The Slantlet transform allows modification during insertion, while the Hessenberg matrix decomposition is applied to the LL subband, which retains the most energy of the image. The Affine transform scrambles the watermark before embedding it in the slice. The hybrid combination of these functions has outperformed previous methods, with good trade-offs between security, imperceptibility, and robustness. The performance measures used, such as NC, PSNR, SNR, and SSIM, indicate good results, with PSNR ranging from 60 to 61 dB, image quality index, and NC all close to one. Furthermore, the simulation results have been tested against image processing threats, demonstrating the effectiveness of this method in ensuring the authenticity and ownership of NIfTI images. Thus, the proposed method in this research provides a reliable and secure solution for the authentication of NIfTI images, which can have significant implications in the healthcare industry.
Movie recommendation model based on probabilistic matrix decomposition using hybrid AdaBoost integration

In recent years, recommendation systems have already played a significant role in major streaming video platforms.The probabilistic matrix factorization (PMF) model has advantages in addressing high-dimension problems and rating data sparsity in the recommendation system. However, in practical application, PMF has poor generalization ability and low prediction accuracy. For this reason, this article proposes the Hybrid AdaBoost Ensemble Method. Firstly, we use the membership function and the cluster center selection in fuzzy clustering to calculate the scoring matrix of the user-items. Secondly, the clustering user items' scoring matrix is trained by the neural network to improve the scoring prediction accuracy further. Finally, with the stability of the model, the AdaBoost integration method is introduced, and the score matrix is used as the base learner; then, the base learner is trained by different neural networks, and finally, the score prediction is obtained by voting results. In this article, we compare and analyze the performance of the proposed model on the MovieLens and FilmTrust datasets. In comparison with the PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models, several experiments show that the mean absolute error of the proposed model increases by 1.24% and 0.79% compared with Bagging-BP-PMF model on two different datasets, and the root-mean-square error increases by 2.55% and 1.87% respectively. Finally, we introduce the weights of different neural network training based learners to improve the stability of the model's score prediction, which also proves the method's universality.
Considering the Consequences of Cybersickness in Immersive Virtual Reality Rehabilitation: A Systematic Review and Meta-Analysis

VR rehabilitation is a rapidly evolving field, with increasing research and development aimed at improving its effectiveness, accessibility, and integration into mainstream healthcare systems. While there are some commercially available VR rehabilitation programs, their adoption and use in clinical practice are still limited. One of the limitations is defined as cybersickness, which is dependent on human contact with virtual reality products. The purpose of this essay is to raise awareness of the associated elements that contribute to cybersickness in rehabilitation using immersive VR. The common factors that influence the amount of cybersickness are user characteristics and device software and hardware. The Simulator Sickness Questionnaire (SSQ) was used as one of the formal models for determining the variables related to virtual reality sickness. The systematic review of the literature and the meta-analysis were chosen by whether the Simulator Sickness Questionnaire in the articles matched the research criteria. Based on PRISMA guidelines, a systematic review of the literature was conducted. Twenty-six publications from the recent past were totaled, comprising 862 individuals with ages ranging from 19 to 95, and 49% were female. The highest overall SSQ mean score for different kinds of symptoms was determined to be 21.058 for brain injuries, with a 95% confidence interval (CI) of 15.357 to 26.760. Time, content, locomotion, control, and display types were other elements that contributed to cybersickness and had significant p-values in the SNK Q-test. The future direction of immersive VR rehabilitation involves the development of immersive and interactive environments that simulate real-world situations, providing patients with a safe and controlled environment in which to practice new skills and movements.
An epistemic logic for formalizing group dynamics of agents

Abstract In the multi-agent setting, it is relevant to model group dynamics of agents, and logic has proved a good tool to do so. We propose an epistemic logic, L-DINF-E, that allows one to formalize what are the beliefs formed by a group of agents, where several groups exist and agents can pass from a group to another one. We introduce a new modality which allows an agent to reason about the beliefs of other agents. This allows us to model aspects of the “Theory of Mind”, understood as the set of social-cognitive skills involving the ability to attribute and reason about mental states, desires, beliefs, and knowledge of agents. In this paper, we present the logic L-DINF-E and illustrate how it can be used to solve “false-belief tasks”, i.e., tests in which an agent should understand that some other agent may develop, under some circumstances, false beliefs.
Learning social navigation from demonstrations with conditional neural processes

Sociability is essential for modern robots to increase their acceptability in human environments. Traditional techniques use manually engineered utility functions inspired by observing pedestrian behaviors to achieve social navigation. However, social aspects of navigation are diverse, changing across different types of environments, societies, and population densities, making it unrealistic to use hand-crafted techniques in each domain. This paper presents a data-driven navigation architecture that uses state-of-the-art neural architectures, namely Conditional Neural Processes, to learn global and local controllers of the mobile robot from observations. Additionally, we leverage a state-of-the-art, deep prediction mechanism to detect situations not similar to the trained ones, where reactive controllers step in to ensure safe navigation. Our results demonstrate that the proposed framework can successfully carry out navigation tasks regarding social norms in the data. Further, we showed that our system produces fewer personal-zone violations, causing less discomfort.
Photovoltaic generation potential and developmental suitability in southern Hebei

The traditional PV potential calculation based on PV available land only considers a single land use type and lacks a comprehensive assessment of all land types, so the PV potential has not been fully exploited. Considering southern Hebei as the study area, this study quantified the utilisation rate of land that was available for photovoltaic (PV) power by GIS and established a PV potential assessment model that considered various land types. Simultaneously, the regional power consumption was spatialised, and the electricity generation and consumption were compared to categorise areas into four types. The results show that the rooftops in southern Hebei had the largest PV potential, with a theoretical power generation capacity of 64,032 GWh, followed by water bodies, roads, and unused land. These areas had a total power generation capacity of 131,306 GWh. Furthermore, power consumption is spatialised due to its uneven spatial distribution. The spatialisation results of power consumption showed that it was relatively concentrated in cities and their surrounding areas. Therefore, rooftops and roads in urban areas are suitable for priority development. When comparing power consumption and generation, it was found that most of the central urban areas and some economically developed counties belonged to the L–H (Low power generation and high power consumption) category and should be developed first.
BARS: a benchmark for airport runway segmentation

Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning (DL), related methods achieve good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of large-scale, publicly available datasets in this field makes the development of methods based on DL difficult. Therefore, we propose a benchmark for airport runway segmentation, named BARS. Additionally, a semiautomatic annotation pipeline is designed to reduce the annotation workload. BARS has the largest dataset with the richest categories and the only instance annotation in the field. The dataset, which was collected using the X-Plane simulation platform, contains 10,256 images and 30,201 instances with three categories. We evaluate eleven representative instance segmentation methods on BARS and analyze their performance. Based on the characteristic of an airport runway with a regular shape, we propose a plug-and-play smoothing postprocessing module (SPM) and a contour point constraint loss (CPCL) function to smooth segmentation results for mask-based and contour-based methods, respectively. Furthermore, a novel evaluation metric named average smoothness (AS) is developed to measure smoothness. The experiments show that existing instance segmentation methods can achieve prediction results with good performance on BARS. SPM and CPCL can effectively enhance the AS metric while modestly improving accuracy. Our work will be available at https://github.com/c-wenhui/BARS .
Fine Aligned Discriminative Hashing for Remote Sensing Image-Audio Retrieval

For cross-modal remote sensing image-audio retrieval task, hashing technology has attracted much attention in recent works. Most of them focus on mapping <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Remote Sensing</i> (RS) images and audios into a Hamming space, whilst neglecting discriminative information of RS images and fine alignment for RS images and audios. In this paper, we tackle these dilemmas with a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fine Aligned Discriminative Hashing</i> (FADH) approach, which can learn hash codes to capture discriminative information of RS images and learn the corresponding detailed information between RS images and audios simultaneously. We first develop a new discriminative information learning module to learn discriminative information of RS images. Meanwhile, a fine alignment module is proposed to unearth the fine correspondence for RS image regions and audios, which can effectively improve the retrieval performance. On top of the two paths, we design a new objective function, which can maintain the similarity of hash codes, preserve the semantic information of RS image features and audio features and eliminate cross-modal differences. The reliability and significance of the designed framework are effectively demonstrated by diverse experiments on three remote sensing image-audio datasets.
Carcara: An Efficient Proof Checker and Elaborator for SMT Proofs in the Alethe Format

Abstract Proofs from SMT solvers ensure correctness independently from implementation, which is often a requirement when solvers are used in safety-critical applications or proof assistants. Alethe is an established SMT proof format generated by the solvers veriT and cvc5, with reconstruction support in the proof assistants Isabelle/HOL and Coq. The format is close to SMT-LIB and allows both coarse- and fine-grained steps, facilitating proof production. However, it lacks a stand-alone checker, which harms its usability and hinders its adoption. Moreover, the coarse-grained steps can be too expensive to check and lead to verification failures. We present Carcara , an independent proof checker and elaborator for Alethe, implemented in Rust. It aims to increase the adoption of the format by providing push-button proof-checking for Alethe proofs, focusing on efficiency and usability; and by providing elaboration for coarse-grained steps into fine-grained ones, increasing the potential success rate of checking Alethe proofs in performance-critical validators, such as proof assistants. We evaluate Carcara over a large set of Alethe proofs generated from SMT-LIB problems and show that it has good performance and its elaboration techniques can make proofs easier to check.
ALFPN: Adaptive Learning Feature Pyramid Network for Small Object Detection

Object detection has become a crucial technology in intelligent vision systems, enabling automatic detection of target objects. While most detectors perform well on open datasets, they often struggle with small-scale objects. This is due to the traditional top-down feature fusion methods that weaken the semantic and location information of small objects, leading to poor classification performance. To address this issue, we propose a novel feature pyramid network, the adaptive learnable feature pyramid network (ALFPN). Our approach features an adaptive feature inspection that incorporates learnable fusion coefficients in the fusion of different levels of feature layers, aiding the network in learning features with less noise. In addition, we construct a context-aligned supervisor that adjusts the feature maps fused at different levels to avoid scaling-related offset effects. Our experiments demonstrate that our method achieves state-of-the-art results and is highly robust for the small object detection on the TT-100K, PASCAL VOC, and COCO datasets. These findings indicate that a model’s ability to extract discriminant features is positively correlated with its performance in detecting small objects.
Optimal Controller and Security Parameter for Encrypted Control Systems Under Least Squares Identification

Encrypted control is a framework for the secure outsourcing of controller computation using homomorphic encryption that allows to perform arithmetic operations on encrypted data without decryption. In a previous study, the security level of encrypted control systems was quantified based on the difficulty and computation time of system identification. This study investigates an optimal design of encrypted control systems when facing an attack attempting to estimate a system parameter by the least squares method from the perspective of the security level. This study proposes an optimal H2 controller that maximizes the difficulty of estimation and an equation to determine the minimum security parameter that guarantee the security of an encrypted control system as a solution to the design problem. The proposed controller and security parameter are beneficial for reducing the computation costs of an encrypted control system, while achieving the desired security level. Furthermore, the proposed design method enables the systematic design of encrypted control systems.
An e-learning evaluation method with the effectiveness of the questionnaire tool in the VLEs

An online evaluation method for the development of more effective tests is presented. The way of evaluation is directed to an existing structure of the use of questionnaires. Empirical-analytical research of quantitative approach is carried out, which evidences an effective evaluation method with the questionnaire tool in the VLE's. A first phase involves a literature review using the PRISMA flow of relevant works from the last three years. A second phase uses the survey technique to a sample of 143 teachers out of a total of 293, who make up the Universidad Politécnica Salesiana, Guayaquil, Ecuador. A confidence level of 95% and a margin of error of 5.9% was achieved. The questions focus on four topics: incorporation of methods and technologies, hybrid education, evaluative tools in VLE's and ICT in education. It is evident that online assessment through questionnaire tools has allowed to support effective learning outcomes.
JumpMod: Haptic Backpack that Modifies Users’ Perceived Jump

Vertical force-feedback is extremely rare in mainstream interactive experiences. This happens because existing haptic devices capable of sufficiently strong forces that would modify a user's jump require grounding (e.g., motion platforms or pulleys) or cumbersome actuators (e.g., large propellers attached or held by the user). To enable interactive experiences to feature jump-based haptics without sacrificing wearability, we propose JumpMod, an untethered backpack that modifies one's sense of jumping. JumpMod achieves this by moving a weight up/down along the user's back, which modifies perceived jump momentum—creating accelerated & decelerated jump sensations. In our second study, we empirically found that our device can render five effects: jump higher, land harder/softer, pulled higher/lower. Based on these, we designed four jumping experiences for VR & sports. Finally, in our third study, we found that participants preferred wearing our device in an interactive context, such as one of our jump-based VR applications.
Multi-view prototype-based disambiguation for partial label learning

In this work, we study the multi-view partial label learning (MVPLL) problem, where each instance is depicted by different view features and associated with a set of candidate labels, among which a true label exists but is inaccessible in the training phase. Most existing PLL methods only consider single-view case, which learn view classifier independently and neglect the view correlations, thus can not be applied to solve MVPLL problem. Due to the non-deep framework, traditional MVPLL approach is weak in the representation ability, so its performance is still to be improved. To solve the MVPLL problem, a deep multi-view prototype-based disambiguation approach is proposed in this paper. Specifically, we innovatively employ the deep neural network for multi-view ambiguously-labeled image classification to enhance the representation ability, which makes use of the information fusion between multiple views. To improve the discriminative ability, we propose multi-view prototype-based label disambiguation algorithm. On theoretical aspect, an estimation error bound for view-risk estimator is established, which is shown to be larger than that for fuse-risk estimator. Experiments demonstrate the superiorities of our proposed method in terms of the prediction accuracy.
Multi-hypothesis representation learning for transformer-based 3D human pose estimation

Despite significant progress, estimating 3D human poses from monocular videos remains a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spatial and temporal relationships. However, those works ignore the fact that it is an inverse problem where multiple feasible solutions (i.e., hypotheses) exist. To relieve this limitation, we propose a Multi-Hypothesis Transformer that learns spatio-temporal representations of multiple plausible pose hypotheses. In order to effectively model multi-hypothesis dependencies and build strong relationships across hypothesis features, we introduce a one-to-many-to-one three-stage framework: (i) Generate multiple initial hypothesis representations; (ii) Model self-hypothesis communication, merge multiple hypotheses into a single converged representation and then partition it into several diverged hypotheses; (iii) Learn cross-hypothesis communication and aggregate the multi-hypothesis features to synthesize the final 3D pose. Through the above processes, the final representation is enhanced and the synthesized pose is much more accurate. Extensive experiments show that the proposed method achieves state-of-the-art results on two challenging datasets: Human3.6M and MPI-INF-3DHP. The code and models are available at https://github.com/Vegetebird/MHFormer.
Metal source and ore precipitation mechanism of the Ashawayi orogenic gold deposit, southwestern Tianshan Orogen, western China: Constraints from textures and trace elements in pyrite

The metal source and ore precipitation mechanism of orogenic gold mineralization are not yet well understood, partly because ore metals may be derived from different sources. Pyrite is a dominant Au-hosting mineral in the Ashawayi orogenic gold deposit in the southwestern Tianshan orogen, western China. Petrographic features of pyrite in host rocks and orebodies define four generations: diagenetic preore (Py1), hydrothermal early-ore (Py2), main-ore (Py3), and late-ore (Py4) pyrites. Trace element abundances were analyzed in situ by femtosecond laser ablation inductively coupled plasma mass spectrometry (fs-LA-ICP-MS) to unravel the pyrite formation history. Preore Py1 contains the lowest Cu, Mo, Se, Au and As contents, consistent with a diagenetic origin. Py2 has higher Au and As contents than Py1 and may have formed by the reaction between hydrothermal fluid and preexisting Py1, as indicated by diagenetic pyrite-like As/Ni and Bi/Au ratios but lower hydrothermal pyrite-like Sb/Au ratios in Py2. Hydrothermal pyrite (Py3) contains more abundant As (1723–65182 ppm) and Au (0.32–107 ppm) but lower Co and Ni contents than Py2, suggesting a greater hydrothermal fluid contribution. Oscillatory zoning and abundant mineral inclusions (e.g., arsenopyrite and chalcopyrite) in porous Py3 indicate that fluid boiling was responsible for gold deposition during the main-ore stage. Py4 is a relict of hydrothermal pyrite (Py3) but not diagenetic pyrite, as supported by Py4 and Py3 clustering into a class based on hierarchical cluster analysis. The application of a machine learning method (i.e., artificial neural network) to the syn-ore pyrite indicates that the Ashawayi gold deposit has affinity to those from orogenic-type gold deposits worldwide. Our study, therefore, highlights that ore metals in orogenic gold deposits may originate from different sources, such as Au and As, which are largely sourced from metamorphic fluids, while Co and Ni are mainly released from preore sedimentary pyrite, fluid boiling and fluid-rock interaction triggered precipitation of Au and other metals.