Reviewerly/exHarmony huggingface dataset:
Fast and precise collision detection for detailed and complex physiological structures

Virtual reality has been proved indispensable in computer-assisted surgery, especially for surgical planning, and simulation systems. Collision detection is an essential part of surgery simulators and its accuracy and computational efficiency play a decisive role in the fidelity of simulations. Nevertheless, current collision detection methods in surgical simulation and planning struggle to meet precise requirements, especially for detailed and complex physiological structures. To address this, the primary objective of this study was to develop a new algorithm that enables fast and precise collision detection to facilitate the improvement of the realism of virtual reality surgical procedures. The method consists of two main parts, bounding spheres formation and two-level collision detection. A specified surface subdivision method is devised to reduce the radius of basic bounding spheres formed by circumcenters of underlying triangles. The spheres are then clustered and adjusted to obtain a compact personalized hierarchy whose position is updated in real time during surgical simulation, followed by two-level collision detection. Triangular facets with collision potential through interaction between hierarchies and then accurate results are obtained by means of precise detection phase. The effectiveness of the algorithm was evaluated in various models and surgical scenarios and was compared with prior relevant implementations. Results on multiple models demonstrated that the method can generate a personalized hierarchy with fewer and smaller bounding spheres for tight wrapping. Simulation experiments proved that the proposed approach is significantly superior to comparable methods under the premise of error-free detection, even for severe model-model collision. The algorithm proposed through this study enables higher numerical efficiency and detection accuracy, which is capable of significantly enlarging the fidelity/realism of haptic simulators and surgical planning methods.
An Evaluation of the Use of Text-Based Comprehensibility Measures on Online Spoken Language Learning Materials

Automated assessment of online spoken materials for use in listening training can be of benefit to language learners. ICALL systems with this facility can help learners sift through collections of online content to find materials that suit their proficiency level and learning goals. Our pilot study assesses the extent to which readability measures can contribute to the listenability assessment of online spoken materials for language learning. Extending these text-based measures to assess listenability is convenient, but their use must be reassessed against spoken materials available online. We compare assessments by four readability measures with expert-assigned assessments of difficulty on online spoken language learning materials. We also evaluate the robustness of these measures against errors arising from automatically generating transcripts and their capability to predict human-based judgments of listenability. Our findings show that these comprehensibility measures can be combined and used to discriminate between materials of different levels of difficulty. We are also able to illustrate a way to conduct a fully automated approach to listenability assessment of spoken language learning materials via text-based measures. Our study also highlights the need for an approach that looks beyond the text, particularly for assessing higher-level materials and spoken materials of different genres.
Vessel Inspection In-the-wild: Practical Planning in Large-scale Industrial Environments

In this paper, a novel strategy for practical inspection planning in dry docks using unmanned aerial vehicles (UAVs) is presented. Planning is a fundamental prerequisite for accurate navigation and control of the UAV. The proposed method utilises the random sample consensus (RANSAC) algorithm to extract plane models from a voxel grid representation of the environment. For high-level planning, semantic knowledge of the environment is leveraged in a novel manner to exploit of structured obstacles, such as straight walls and orthogonal corners. In order to deal with lower-level navigation, the approach incorporates a simple graph-based local replanner to generate paths that avoid obstacles in the environment. The proposed method is compared with state-of-the-art graph-based planner in simulation and subsequently evaluated in a real environment. The paper maintains the use case of UAV vessel inspection and presents exhaustive simulation and field testing, which demonstrate the viability of the proposed approach in a fully working large-scale industrial environment.
Expanded low-rank parity-check codes and their application to cryptography

In this paper, we define expanded LRPC codes from LRPC codes with a decoding algorithm using the one of the underlying LRPC codes. Next, we propose to use these codes for cryptography by deriving two cryptosystems in a McEliece setting. in order to reduce the key sizes, we use a generator matrix in systematic form for the first scheme, and an m− order quasi-cyclic LRPC code that we define for the second scheme. The obtained code has a very poor structure and is more likely to be a random linear code. Next, we give some security parameters and compare the key sizes of our public key with the key sizes of some cryptosystems.
Maturity model for secure software testing

Abstract Security is an essential attribute of high‐quality software. However, effectively incorporating security practices into different phases of the software development life cycle (SDLC) remains challenging. Owing to less mature secure testing processes, organizations are prone to ineffective testing practices for defect detection, including severe security‐related failures. Thus, in this study, we present a maturity model for secure software testing (MMSST) to assist software development organizations in improving the secure testing of software applications. We conducted a multivocal literature review and identified 68 primary studies from the formal and gray literature. Then, based on the available evidence, 27 process areas were identified to develop the proposed MMSST. The MMSST includes five main categories: governance, contrive and design, execution, deployment and configuration, and mature. The MMSST was subsequently evaluated using case studies related to practical environments. Results demonstrate that the proposed MMSST is useful for estimating the maturity level of an organization with respect to the secure testing phase of the SDLC. The participants of the case studies also agreed that the proposed MMSST is useful in terms of structure, user satisfaction, and ease of use. We believe that the proposed MMSST can help organizations evaluate and improve software security testing practices. In addition, the proposed MMSST is expected to provide researchers and industry practitioners with an effective foundation for developing new secure testing approaches and tools.
The Use of Voice Assistant for Psychological Assessment Elicits Empathy and Engagement While Maintaining Good Psychometric Properties

This study aimed to use the Alexa vocal assistant as an administerer of psychometric tests, assessing the efficiency and validity of this measurement. A total of 300 participants were administered the Interpersonal Reactivity Index (IRI). After a week, the administration was repeated, but the participants were randomly divided into groups of 100 participants each. In the first, the test was administered by means of a paper version; in the second, the questionnaire was read to the participants in person, and the operator contemporaneously recorded the answers declared by the participants; in the third group, the questionnaire was directly administered by the Alexa voice device, after specific reprogramming. The third group was also administered, as a post-session survey, the Engagement and Perceptions of the Bot Scale (EPVS), a short version of the Communication Styles Inventory (CSI), the Marlowe-Crowne Social Desirability Scale (MCSDS), and an additional six items to measure degrees of concentration, ease, and perceived pressure at the beginning and at the end of the administration. The results confirmed that the IRI did keep measurement invariance within the three conditions. The administration through vocal assistant showed an empathic activation effect significantly superior to the conditions of pencil-paper and operator-in-presence. The results indicated an engagement and positive evaluation of the interactive experience, with reported perceptions of closeness, warmth, competence, and human-likeness associated with higher values of empathetic activation and lower values of personal discomfort.
Explainable Artificial Intelligence on the Battlefield (Military-XAI, MXAI) : Research for Military Application Scenarios

The most significant weakness of AI technology is the lack of explainability of the results obtained by AI. The technology that has emerged to compensate for this weakness of traditional AI is eXplainable AI (XAI). XAI is attracting great attention in applications where the reliability of inference results is important, such as healthcare, defense, law, and has recently been judged to be indispensable for military applications. The U.S. Defense Advanced Research Projects Agency (DARPA) is actively promoting the application of XAI in the military field by operating the XAI Program, in which 11 universities and research institutes such as UC Berkeley and Carnegie Mellon University participate, but in the case of the Korean military, research on XAI development is underdeveloped due to the focus on developing AI capabilities. In this paper, we also examine the concept and methodology of XAI and propose a method for applying XAI technology that can be applied to AI technology for each battlefield functions. The concept and recent technology of XAI are summarized, and the concept of XAI operation and application scenario of AI that is expected to be applied to the weapon system for each battlefield function is presented.
Payment Security Issues and User Data in Online Commerce

In recent years, there has been increased development of infor­mation and telecommunication technologies, which have a significant im­pact on the economic and social sphere on a global scale and, in particular, at the national level. In parallel, there are many challenges to the integra­tion and use of digital technologies and the resulting issues of payment and data security in online commerce. This research is dedicated to revealing the nature of data privacy, the rela­tionship between the regulator, consumers and retailers, and presenting the features of payment and data security in online commerce.
Why Shallow Networks Struggle with Approximating and Learning High Frequency: A Numerical Study

In this work, a comprehensive numerical study involving analysis and experiments shows why a two-layer neural network has difficulties handling high frequencies in approximation and learning when machine precision and computation cost are important factors in real practice. In particular, the following basic computational issues are investigated: (1) the minimal numerical error one can achieve given a finite machine precision, (2) the computation cost to achieve a given accuracy, and (3) stability with respect to perturbations. The key to the study is the conditioning of the representation and its learning dynamics. Explicit answers to the above questions with numerical verifications are presented.
An Oblivious Stochastic Composite Optimization Algorithm for Eigenvalue Optimization Problems

In this work, we revisit the problem of solving large-scale semidefinite programs using randomized first-order methods and stochastic smoothing. We introduce two oblivious stochastic mirror descent algorithms based on a complementary composite setting. One algorithm is designed for non-smooth objectives, while an accelerated version is tailored for smooth objectives. Remarkably, both algorithms work without prior knowledge of the Lipschitz constant or smoothness of the objective function. For the non-smooth case with $\mathcal{M}-$bounded oracles, we prove a convergence rate of $ O( {\mathcal{M}}/{\sqrt{T}} ) $. For the $L$-smooth case with a feasible set bounded by $D$, we derive a convergence rate of $ O( {L^2 D^2}/{(T^{2}\sqrt{T})} + {(D_0^2+\sigma^2)}/{\sqrt{T}} )$, where $D_0$ is the starting distance to an optimal solution, and $ \sigma^2$ is the stochastic oracle variance. These rates had only been obtained so far by either assuming prior knowledge of the Lipschitz constant or the starting distance to an optimal solution. We further show how to extend our framework to relative scale and demonstrate the efficiency and robustness of our methods on large scale semidefinite programs.
Exploration of the influence of early rehabilitation training on circulating endothelial progenitor cell mobilization in patients with acute ischemic stroke and its related mechanism under a lightweight artificial intelligence algorithm.

This work aimed to explore the application of lightweight artificial intelligence algorithms in magnetic resonance imaging (MRI) image processing of patients with acute ischemic stroke (AIS) to clarify the effect and mechanism of early rehabilitation training on the mobilization of circulating endothelial progenitor cells (EPCs) in AIS.A total of 98 AIS patients undergoing MRI examination were selected as the research objects and were randomly divided into a rehabilitation group (early rehabilitation training, 50 cases) and a routine group (conventional treatment, 48 cases) by random number table method and lottery method. In this work, based on the convolutional neural network (CNN) algorithm, a low-rank decomposition algorithm was introduced to optimize it, and a lightweight MRI image computer intelligent segmentation model (LT-RCNN) was established. The LT-RCNN model was used in the MRI image processing of AIS patients, and the role of the model in AIS image segmentation and lesion localization was analyzed. Furthermore, flow cytometry was used to detect the number of peripheral circulating EPCs and CD34+KDR+ cells in the two groups of patients before and after treatment. The serum levels of vascular endothelial growth factor (VEGF), tumor necrosis factor-α (TNF-α), interleukin 10 (IL-10), and stromal cell-derived factor-1α (SDF-1α) content were detected by Enzyme-Linked Immunosorbent Assay (ELISA). In addition, the correlation between each factor and CD34+KDR+ was analyzed by Pearson linear correlation.The diffusion-weighted imaging (DWI) signal of MRI images of AIS patients under the LT-RCNN model was high. The location of the lesion could be accurately detected, and the contour of the lesion could be displayed and segmented, and the segmentation accuracy and sensitivity were significantly better than before optimization. The number of EPCs and CD34+KDR+ cells in the rehabilitation group was increased compared with the control group (p<0.01); the expression levels of VEGF, IL-10, and SDF-1α were higher than those of the control group (p<0.001), and TNF-α content was lower than the control group (p<0.001). The number of CD34+KDR+ cells was positively correlated with VEGF, IL-10, and TNF-α contents (p<0.01).The results showed that the computer-intelligent segmentation model LT-RCNN could accurately locate, and segment AIS lesions and the early rehabilitation training could change the expression level of inflammatory factors and further promote the mobilization of AIS circulation EPCs.
Multi-Agent Active Search using Detection and Location Uncertainty

Active search, in applications like environment monitoring or disaster response missions, involves autonomous agents detecting targets in a search space using decision making algorithms that adapt to the history of their observations. Active search algorithms must contend with two types of uncertainty: detection uncertainty and location uncertainty. The more common approach in robotics is to focus on location uncertainty and remove detection uncertainty by thresholding the detection probability to zero or one. In contrast, it is common in the sparse signal processing literature to assume the target location is accurate and instead focus on the uncertainty of its detection. In this work, we first propose an inference method to jointly handle both target detection and location uncertainty. We then build a decision making algorithm on this inference method that uses Thompson sampling to enable decentralized multi-agent active search. We perform simulation experiments to show that our algorithms outperform competing baselines that only account for either target detection or location uncertainty. We finally demonstrate the real world transferability of our algorithms using a realistic simulation environment we created on the Unreal Engine 4 platform with an AirSim plugin.
Operational aspects of forming educational activity culture of future teachers in the poliparadigm educational space

Introduction. The system of teacher education in Russia has been in a state of reform for several decades. There is an intensive search for technologies and approaches that could provide training for teaching staff who are able to withstand the challenges of the 21st century. Methodological understanding of the value and theoretical foundations of the design of the educational process in a pedagogical university leads to the conclusion about the plurality of pedagogical paradigms that determine the educational space and the need to take this plurality into account in the organization of the educational process. Theoretical analysis. The article attempts to identify the dominant paradigms in the space of higher education and show their meaningful characteristics. An analysis of the correlation of paradigms focused on results (competence-based and student-oriented) and on all components of the educational process (technocratic) is given. Their features and essential characteristics are highlighted. The conditions under which at the operational level it becomes possible to realize the task of forming a culture of educational activity are analyzed. Conclusion. The main conclusion of the proposed theoretical analysis is the impossibility to fully implement the task of forming a culture of educational activity in the conditions of the dominance of the technocratic paradigm. But it is quite possible to create conditions that allow this possibility.
Automatic Generation of Robot Facial Expressions with Preferences

The capability of humanoid robots to generate facial expressions is crucial for enhancing interactivity and emotional resonance in human-robot interaction. However, humanoid robots vary in mechanics, manufacturing, and ap-pearance. The lack of consistent processing techniques and the complexity of generating facial expressions pose significant challenges in the field. To acquire solutions with high confidence, it is necessary to enable robots to explore the solution space automatically based on performance feedback. To this end, we designed a physical robot with a human-like appearance and developed a general framework for automatic expression generation using the MAP-Elites algorithm. The main advan-tage of our framework is that it does not only generate facial expressions automatically but can also be customized according to user preferences. The experimental results demonstrate that our framework can efficiently generate realistic facial expressions without hard coding or prior knowledge of the robot kinematics. Moreover, it can guide the solution-generation process in accordance with user preferences, which is desirable in many real-world applications.
Multi-Agent Path Integral Control for Interaction-Aware Motion Planning in Urban Canals

Autonomous vehicles that operate in urban environments shall comply with existing rules and reason about the interactions with other decision-making agents. In this paper, we introduce a decentralized and communication-free interaction-aware motion planner and apply it to Autonomous Surface Vessels (ASVs) in urban canals. We build upon a sampling-based method, namely Model Predictive Path Integral control (MPPI), and employ it to, in each time instance, compute both a collision-free trajectory for the vehicle and a prediction of other agents' trajectories, thus modeling interactions. To improve the method's efficiency in multi-agent scenarios, we introduce a two-stage sample evaluation strategy and define an appropriate cost function to achieve rule compliance. We evaluate this decentralized approach in simulations with multiple vessels in real scenarios extracted from Amsterdam's canals, showing superior performance than a state-of-the-art trajectory optimization framework and robustness when encountering different types of agents.
Impact of Metaverse in the Near ‘Future’

Ever since Neel Stephenson's novel ‘Snow Crash’ was published in 1992, the metaverse and its virtual space have been evolving. Because of the growing desire for immersion, this survey article is the first endeavor to offer a comprehensive framework that assesses the contemporary metaverse development under the perspectives of state-of-the-art innovations and digital world ecosystems. So, we re-investigate eight enabling technologies: Extended Reality, User Interactivity, Artificial Intelligence, Blockchain, Computer Vision, IoT and Robots, Future Mobile Networks etcetera. The metaverse environment in application terms means empowering users to interact with and live within a subconscious everlasting and shared reality. A wide range of computer-mediated virtual environments that we have created include social networks, video conferencing, virtual 3D worlds (like VR Chat), augmented reality apps (like Pokemon Go), and non-fungible token games (like Upland).Although transient and unconnected, these virtual environments have brought about various levels of digital change for us. Thus, we propose clear future research for the metaverse's advancement.
Optimal and Asymptotically Good Locally Repairable Codes via Propagation Rules

In classical coding theory, it is common to construct new codes via propagation rules. There are various propagation rules to construct classical block codes. However, propagation rules have not been extensively explored for locally repairable codes. In this paper, we systematically study some of propagation rules to construct optimal and asymptotically good locally repairable codes. To our surprise, these simple propagation rules produce interesting results. Firstly, by a lengthening propagation rule that adds some rows and columns to a parity-check matrix of a given linear code, we are able to convert a classical maximum distance separable (MDS) code into a Singleton-optimal locally repairable code and provide a simplified proof of the asymptotic Tafasman-Vlăduţ-Zink bound which exceeds the asymptotic Gilbert-Varshamov bound of locally repairable codes. Secondly, by concatenating a locally repairable code as an inner code with a classical block code as an outer code, we obtain a family of dimension-optimal locally repairable codes. Thirdly, we can make use of the shortening technique to produce more dimension-optimal locally repairable codes. Finally, one of phenomena that we observe in this paper is that some trivial propagation rules in classical block codes do not hold anymore for locally repairable codes.