Reviewerly/exHarmony huggingface dataset:
Exploring the Potential of Ensembles of Deep Learning Networks for Image Segmentation

To identify objects in images, a complex set of skills is needed that includes understanding the context and being able to determine the borders of objects. In computer vision, this task is known as semantic segmentation and it involves categorizing each pixel in an image. It is crucial in many real-world situations: for autonomous vehicles, it enables the identification of objects in the surrounding area; in medical diagnosis, it enhances the ability to detect dangerous pathologies early, thereby reducing the risk of serious consequences. In this study, we compare the performance of various ensembles of convolutional and transformer neural networks. Ensembles can be created, e.g., by varying the loss function, the data augmentation method, or the learning rate strategy. Our proposed ensemble, which uses a simple averaging rule, demonstrates exceptional performance across multiple datasets. Notably, compared to prior state-of-the-art methods, our ensemble consistently shows improvements in the well-studied polyp segmentation problem. This problem involves the precise delineation and identification of polyps within medical images, and our approach showcases noteworthy advancements in this domain, obtaining an average Dice of 0.887, which outperforms the current SOTA with an average Dice of 0.885.
Privacy-First Paradigm for Dynamic Consent Management Systems: Empowering Data Subjects through Decentralized Data Controllers and Privacy-Preserving Techniques

This paper explicitly focuses on utilizing blockchain technology in dynamic consent management systems with privacy considerations. While blockchain offers improved security, the potential impact on entities’ privacy must be considered. Through a critical investigation of available contributions to the present state of the art of blockchain-based dynamic consent management systems, we highlight the limitations of plaintext storage and the processing of subject data/consent on the blockchain, which can compromise privacy. We stress the significance of keeping encrypted subject data/consent on the blockchain and sharing it in encrypted form with data controllers and requesters to guarantee privacy and security. Our proposed model demonstrates the usefulness of privacy-preserving techniques, underscoring the decentralization of the abstract entity data controller to enhance subject data/consent privacy. Additionally, we suggest the integration of privacy-enhancing technologies such as secure multi-party computation, homomorphic encryption, and differential privacy with blockchain to accomplish both security and privacy, aligning with the data sharing practices outlined in the General Data Protection Regulation (GDPR) in Europe.
A route planning for oil sample transportation based on improved A* algorithm

The traditional A* algorithm suffers from issues such as sharp turning points in the path, weak directional guidance during the search, and a large number of computed nodes. To address these problems, a modified approach called the Directional Search A* algorithm along with a path smoothing technique has been proposed. Firstly, the Directional Search A* algorithm introduces an angle constraint condition through the evaluation function. By converting sharp turns into obtuse angles, the path turning points become smoother. This approach reduces the occurrence of sharp turns in the path, resulting in improved path smoothness. Secondly, the algorithm enhances the distance function to strengthen the directional guidance during the path search. By optimizing the distance function, the algorithm tends to prefer directions that lead towards the target, which helps reduce the search space and shorten the overall path planning time. Additionally, the algorithm removes redundant nodes along the path, resulting in a more concise path representation. Lastly, the algorithm proposes an improved step size adjustment method to optimize the number of path nodes obtained. By appropriately adjusting the step size, the algorithm further reduces the number of nodes, leading to improved path planning efficiency. By applying these methods together, the Directional Search A* algorithm effectively addresses the limitations of the traditional A* algorithm and produces smoother and more efficient path planning results. Simulation experiments comparing the modified A* algorithm with the traditional A* algorithm were conducted using MATLAB. The experimental results demonstrate that the improved A* algorithm can generate shorter paths, with reduced planning time and smoother trajectories. This indicates that the Directional Search A* algorithm, incorporating the angle constraint condition in the evaluation function and the direction-guided strategy, outperforms the traditional A* algorithm in path planning and provides better solutions to the existing issues.
Toward Accurate Post-Born–Oppenheimer Molecular Simulations on Quantum Computers: An Adaptive Variational Eigensolver with Nuclear-Electronic Frozen Natural Orbitals

Nuclear quantum effects such as zero-point energy and hydrogen tunneling play a central role in many biological and chemical processes. The nuclear-electronic orbital (NEO) approach captures these effects by treating selected nuclei quantum mechanically on the same footing as electrons. On classical computers, the resources required for an exact solution of NEO-based models grow exponentially with system size. By contrast, quantum computers offer a means of solving this problem with polynomial scaling. However, due to the limitations of current quantum devices, NEO simulations are confined to the smallest systems described by minimal basis sets, whereas realistic simulations beyond the Born-Oppenheimer approximation require more sophisticated basis sets. For this purpose, we herein extend a hardware-efficient ADAPT-VQE method to the NEO framework in the frozen natural orbital (FNO) basis. We demonstrate on H2 and D2 molecules that the NEO-FNO-ADAPT-VQE method reduces the CNOT count by several orders of magnitude relative to the NEO unitary coupled cluster method with singles and doubles while maintaining the desired accuracy. This extreme reduction in the CNOT gate count is sufficient to permit practical computations employing the NEO method─an important step toward accurate simulations involving nonclassical nuclei and non-Born-Oppenheimer effects on near-term quantum devices. We further show that the method can capture isotope effects, and we demonstrate that inclusion of correlation energy systematically improves the prediction of difference in the zero-point energy (ΔZPE) between isotopes.
The Place of Descriptive Geometry in the Face of Industry 4.0 Challenges

Industrial process automation has long been the main goal in production lines that seek to decrease human involvement. However, it is broadly agreed that a collaboration between humans and technologies must still exist as human capital is required to provide certain skills and abilities that machines cannot offer yet. For instance, in the context of design and simulation, CAD, CAM, and CAE professionals must count not only on a deep knowledge of the technology employed but also on specific skills that make the human factor an integral piece of the transition. These abilities are considered fundamental to achieving sustainable development in the industrial sector. This work focuses on analyzing through four study cases where whether specular o bilateral symmetry, a.k.a. planar symmetry is present, the weaknesses found in the human factor related to CAD training of future industrial engineers. The most common mistakes found when developing the different symmetric pieces proposed are thoroughly examined in order to define their origin, which mainly lies in students’ lack of descriptive geometry (DG) understanding. This is aggravated in some cases by the lack of spatial visualization abilities. The unstoppable and fast advances in design and simulation tools and technologies require humans to update their capabilities almost in real time. However, results show that this should not threaten the need for the human mind to spatially understand the changes being made on the screen. Otherwise, humans are at risk of ending up at the service of machines and technologies instead of the opposite.
Probing a Hybrid Channel for the Dynamics of Non-Local Features

Effective information transmission is a central element in quantum information protocols, but the quest for optimal efficiency in channels with symmetrical characteristics remains a prominent challenge in quantum information science. In light of this challenge, we introduce a hybrid channel that encompasses thermal, magnetic, and local components, each simultaneously endowed with characteristics that enhance and diminish quantum correlations. To investigate the symmetry of this hybrid channel, we explored the quantum correlations of a simple two-qubit Heisenberg spin state, quantified using measures such as negativity, ℓ1-norm coherence, entropic uncertainty, and entropy functions. Our findings revealed that the hybrid channel can be adeptly tailored to preserve quantum correlations, surpassing the capabilities of its individual components. We also identified optimal parameterizations to attain maximum entanglement from mixed entangled/separable states, even in the presence of local dephasing. Notably, various parameters and quantum features, including non-Markovianity, exhibited distinct behaviors in the context of this hybrid channel. Ultimately, we discuss potential experimental applications of this configuration.
Identifying Public Tenders of Interest Using Classification Models: A Comparative Analysis

Abstract Public procurement processes related to tenders represent one of the most important financing strategies for companies in diverse sectors, as they present the opportunity to offer services, supplies and product sales to state entities. Given the high volume of public tenders in the Colombian Government SECOP II database, manually identifying tenders of interest can be a cumbersome and time-consuming process. In this work, we propose automating the identification of interesting tenders by training a supervised classification model. We manually label a sample of tenders published in the National Government open data platform, according to whether or not they are of interest to the Minuto de Dios University, and use them for model training. Several models are evaluated in order to select the best model for deployment, taking into account various metrics to determine best performance according to business needs. The best model is selected based on different analyses, comparing the application of data balancing techniques, the performance of the proposed models, and hyper-parameter settings measured against the test data.
Enhancing Virtual Experiences: A Holistic Approach to Immersive Special Effects

To create a more immersive experience, electronic content developers utilize hardware solutions that not only display images and produce sounds but also manipulate the viewer’s real environment. These devices can control visual effects like lighting variations and fog, emit scents, simulate liquid effects, and provide vibration or locomotion sensations, such as moving the viewer’s chair. The goal is to emulate additional sensations for the viewers and engender the belief that they are truly present within the virtual environment. These devices are typically found in specially designed cinemas referred to as xD cinemas, such as 4D, 5D, 9D, etc., where each effect is treated as an additional dimension, enhancing the overall experience. Currently, all of these effects are triggered by timers. The system determines which effect to play based on timers. This approach is problematic, for it requires programming each device for each movie. In this research, we address this problem by introducing the idea of Special Effect Tags (SETs) that can be added in the subtitle files. The SETs aim to serve as a standard that will allow the various devices to know when each artificial phenomenon should be triggered. They are generic and can support infinite artificial phenomena, also known as dimensions. This paper introduces the idea of a common special effect framework and a generic architecture of a special effects player that is independent of any specific hardware solutions.
Recent Developments in Machine Learning Predictive Analytics for Disaster Resource Allocation

To be effective, evidence-driven disaster risk management (DRM) relies on a wide variety of data types, information sources, and models. Weather modeling, the rupture of earthquake fault lines, and the creation of dynamic urban exposure measures all require extensive data collection from a variety of sources in addition to complex science. There are various methodologies to utilize AI to recognize necessities and asset accessibility by the likes of Twitter; however, the foremost broadly recognized and exact strategies remain cloudy. Within the occurrence of a catastrophe, machine learning apparatuses for designating assets are required to instantly help those in need. This overview appears to be necessary for additional examination with respect to an assertion on endorsed methods for calculation to demonstrate assurance, benchmarking datasets, crisis word references, word embedding techniques, and evaluation methods. As fiascos of all sorts become more common, these devices have the potential to improve real-time crisis administration over all stages of a catastrophe. This study aims to provide readers, including data scientists, with a clear and uncomplicated reference on how disaster risk management systems can benefit from machine learning. There are numerous sources of information on this set of technologies, which are both complicated and constantly changing. The volume of sensor data that can be analyzed has increased exponentially because of enormous increases in computational speed and capacity over the past few decades.
Progressive Reservation of Cloud Services Using Multi-Cloud Broker System

Cloud brokers play a crucial role in providing an effective service by utilizing cloud computing. The middleware known as cloud brokers aids in the provision of effective cloud services to cloud users. There are a lot of cloud brokers who offer cloud services to cloud users on a reservation-in-advance basis so that they do not have to rush to use the cloud. A cloud organization agent is IT work and an arrangement of activities in which an organization or other component improves at least one cloud organization for the better and at least one buyer of that help through three basic employments: counting combination, joining, and customization trade. Since cloud innovation offers a Cloud Benefit Brokerage stage for them to run their delicate and basic operations, it has gotten to be exponentially acknowledged by businesses all over the world. A cloud organization provider has to provide a profitable strategy for restricting the induction to the distinctive components of the cloud that the board organizes. The clients should be able to safely construct, create, and spare their claim reports, and there should be a centralized database detailing information from different lives of clients. Utilizing the proposed methodology system, users are provided with the reserved services that best suit their needs. Effective services were the subject of this research paper. The proposed system performs best due to its complexity.
Resource-efficient fault-tolerant one-way quantum repeater with code concatenation

Abstract One-way quantum repeaters where loss and operational errors are counteracted by quantum error-correcting codes can ensure fast and reliable qubit transmission in quantum networks. It is crucial that the resource requirements of such repeaters, for example, the number of qubits per repeater node and the complexity of the quantum error-correcting operations are kept to a minimum to allow for near-future implementations. To this end, we propose a one-way quantum repeater that targets both the loss and operational error rates in a communication channel in a resource-efficient manner using code concatenation. Specifically, we consider a tree-cluster code as an inner loss-tolerant code concatenated with an outer 5-qubit code for protection against Pauli errors. Adopting flag-based stabilizer measurements, we show that intercontinental distances of up to 10,000 km can be bridged with a minimized resource overhead by interspersing repeater nodes that each specialize in suppressing either loss or operational errors. Our work demonstrates how tailored error-correcting codes can significantly lower the experimental requirements for long-distance quantum communication.
Learning based multi-obstacle avoidance of unmanned aerial vehicles with a novel reward

In this paper, a novel reward-based learning method is proposed for unmanned aerial vehicles to achieve multi-obstacle avoidance. The Markov jump model was first formulated for the unmanned aerial vehicle obstacle avoidance problem. A distinctive reward shaping function is proposed to adaptively avoid obstacles and finally reach the target position via an optimal approach such that an adaptive Q-learning algorithm called the improved prioritized experience replay is developed. Simulation results show that the proposed algorithm can achieve autonomous obstacle avoidance in complex environments with improved performance.
Basics of Second-Order Predicate Logic

The article presents the basics of second-order predicate logic (SOL). The need for a symbolic representation of the general quantifier is pointed out. A distinction is made between the first-order predicate logic (FOL) and the second-order predicate logic (predicates of predicates, relations of relations). The syntax and semantics of the second-order predicate logic are introduced. Logical and non-logical designators and operators, terms, rules for forming logical formulas, status of variables, and rules for variable substitution are introduced. Reference is made to Henkin's semantics of controlled predicates, and an axiomatic system of second-order predicate logic. Russell's analogy for the axiom of choice and methods of proving the validity of the deduction for second-order predicate logic are given.
A comparative cyber risk analysis between federated and self-sovereign identity management systems

Abstract Self-sovereign identity (SSI) is an emerging and promising concept that enables users to control their identity while enhancing security and privacy compared to other identity management (IDM) approaches. Despite the recent advancements in SSI technologies, federated identity management (FIDM) systems continue to dominate the IDM market. Selecting an IDM to implement for a specific application is a complex task that requires a thorough understanding of the potential external cyber risks. However, existing research scarcely compares SSI and FIDM from the perspective of these external threats. In response to this gap, our article provides an attack surface analysis focused solely on external threats for both systems. This analysis can serve as a reference to compare the relevant security and privacy risks associated with these external threats. The threat landscapes of external attackers were systematically synthesized from the main components and functionalities of the common standards and designs. We further present a use case analysis that applies this attack surface analysis to compare the external cyber risks of the two systems in detail when managing cross-border identity between European countries. This work can be particularly useful for considering a more secure design for future IDM applications, taking into account the landscape of external threats.
Social Media Discourse: Towards the Problem of Interpretation

The subject of research in this article is the social media discourse. The purpose of the work is to theoretically interpret the social media discourse in the context of modern science and practice of media communications. The study of social media discourse considers the features of the platforms, its production and storage, as well as its representation. Social media users can change their roles from being a consumer to a creator. The work uses a set of methods: comparative analysis, systematization, and interpretative analysis. The research found that the social media discourse is a functionally conditioned type of discourse (integration of personal and institutional types of discourse) that exists in the framework of online communication, and has the synthesis of oral and written speech. The social media discourse consists of media texts that include heterogeneous elements, depending on the combination of which creolized, polycode and multimodal texts are constructed. Such texts differ in the process of creation, organization and features of presentation in social media, communication in which, unlike electronic communication in general, is based on the principle of participation. Taking into account the specific features of distribution platforms used by social media, the authors emphasize that all Internet services related to social media have special characteristics that make it possible to distinguish them from other Internet services, resources that exist in the global network. The definition of the social media discourse allows us to expand the understanding of this category in modern media communications.
Analisis Metode K-Medoids Cluster Dalam Mengelompokkan Siswa Yang Berprestasi

Cluster analysis of outstanding students using data mining, namely the K-Medoid Cluster Algorithm. Previously, the school still used the manual method in determining students who excel at the school, so it took a long time and the results were not accurate. K-Medoid Cluster is one of the algorithms used for data classification or grouping, the authors apply the K-Medoid Cluster algorithm in grouping students with high achievement in order to get more accurate, fast, and effective results.
Bridging Realities: Understanding the Factors Influencing Visitor Satisfaction and Authentic Experiences in Virtual Tourism

The viability of virtual tourism (VT) as a feasible alternative or interim solution has been heavily debated considering the growing interest in virtual reality (VR) tourism and its relevance. There is, however, no information on how to keep visitors satisfied with VT, enhance their contentment, and turn good experiences into genuine travel ambitions. This paper presents a conceptual model that blends the uses and gratifications theory with the innovation diffusion theory to understand the drivers of VR tourism. The main objective is to explore the factors related to innovation diffusion and gratification that contribute to tourist satisfaction and authentic experience in VT. The study also attempts to find out how satisfaction and authentic experience affect the desire to go to suggested places. Furthermore, the study looks at the function of connection to nature as a moderator in the link between satisfaction and on-site destination intention. The PLS-SEM analysis was used to evaluate the model on a sample of 285 respondents. The findings show that variables related to innovation diffusion, uses, and gratifications have a considerable impact on visitor satisfaction and authentic experience. Furthermore, as compared to authentic experience, visitor satisfaction has a larger effect on the intention to visit the place. However, as the amount of connection to nature grows, the favourable effect of VT satisfaction on on-site tourist intention increases. This study adds to the uses and gratifications theory and the innovation diffusion theory, giving significant insights for tourism practitioners and management looking to improve visitor satisfaction and authentic experience with VT and other attractions.