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Background: The increasing demand for remote medical care, driven by digital healthcare advancements and the COVID-19 pandemic, necessitates effective solutions tailored to patients and healthcare practitioners. Co-design, involving collaboration between software developers, patients, and healthcare practitioners, prioritizes end-user needs. Research indicates that integrating patient perspectives enhances user experience and usability. However, its application in healthcare has been limited to small projects. This work focuses on co-designing a technological solution to enhance the monitoring and visual rehabilitation of individuals with Age-Related Macular Degeneration (AMD), a condition that significantly impacts the quality of life in people over 60. Current vision rehabilitation systems lack personalization, motivation, and effective progress monitoring. Involving patients and healthcare practitioners in the design process aims to ensure the final product meets their needs. Methods: The project employs iterative and collaborative principles, involving a vision rehabilitation expert and two AMD patients as active users in the application's development and validation. The process begins by establishing requirements for user accounts and rehabilitation exercises. It continues with an initial approach extended through user validation. Co-design is facilitated by specific workshops marking each project iteration, totaling four workshops, along with continuous communication sessions between experts and developers to validate design decisions. Initial requirements gathering and constant feedback from end-users, the visual rehabilitator, and patients are crucial for refining the product effectively. Results: The workshops produced a prototype featuring a test to monitor changes and progression and 15 visual rehabilitation exercises. Numerous patient and vision rehabilitation-driven software modifications led to a final design that is responsive and adaptive to end-user needs. Conclusions: The Rehab-AMD pilot project aims to develop a collaborative and adaptive software solution for AMD rehabilitation by actively involving stakeholders and applying iterative design principles. Co-design in the Rehab-AMD solution proves to be a methodology that identifies usability issues and needs from the initial design stages. This approach ensures that software developers create a final product that is genuinely useful and manageable for people with AMD and the targeted vision rehabilitators. [ABSTRACT FROM AUTHOR]

Machine learning-based image super-resolution (SR) has garnered increasing research interest in recent years. However, there are two issues that have not been adequately addressed. The first issue is that existing SR methods often overlook the importance of improving the quality of the training dataset, which is a crucial factor in determining SR performance, regardless of the training method employed. The second issue is that while some studies report high numerical metrics, the visual results remain unsatisfactory. To address the first problem, we propose a new image down-sampling method to obtain higher-quality training datasets. To tackle the second problem, we present a new image super-resolution model based on a large-size convolution kernel and a multi-path algorithm. Specifically, we use an adaptive large-size convolutional kernel to extract features from the image based on the size of the input image, and a residual network to generate a deeper model to retain more details of the original input image. Experimental results demonstrate that the proposed multilayer downsampling method (MDM) can significantly improve the visual quality compared to traditional downsampling methods. Moreover, our proposed method achieves the best peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) values compared to several typical SR algorithms. Furthermore, subjective evaluation by human observers reveals that our method retains more details of the original image and produces smoother high-resolution images. Our proposed method effectively addresses the two aforementioned issues, which leads to improved SR performance in terms of both quantitative and qualitative measures. [ABSTRACT FROM AUTHOR]

In the current digital landscape, artificial intelligence-driven automation has revolutionized efficiency in various areas, enabling significant time and resource savings. However, the reliability and efficiency of software systems remain crucial challenges. To address this issue, a generation of self-adaptive software has emerged with the ability to rectify errors and autonomously optimize performance. This study focuses on the development of self-adaptive software designed for pre-programmed tasks on the Internet. The software stands out for its self-adaptation, automation, fault tolerance, efficiency, and robustness. Various technologies such as Python, MySQL, Firebase, and others were employed to enhance the adaptability of the software. The results demonstrate the effectiveness of the software, with a continuously growing self-adaptation rate and improvements in response times. Probability models were applied to analyze the software's effectiveness in fault situations. The implementation of virtual cables and multiprocessing significantly improved performance, achieving higher execution speed and scalability. In summary, this study presents self-adaptive software that rectifies errors, optimizes performance, and maintains functionality in the presence of faults, contributing to efficiency in Internet task automation. [ABSTRACT FROM AUTHOR]

As the CMOS process continues to decrease in size, the latch becomes increasingly vulnerable to the triple-node-upset (TNU), leading to a decrease in circuit reliability in harsh radiation environments. In this paper, a low-cost TNU self-recovery latch (LCTR) is proposed, which is based on one PMOS and two NMOS (1P2N) inverters and C-elements (CEs) forming a multi-level feedback loop. Due to the error interception function of these fundamental elements, the proposed latch completely realizes TNU self-recovery capability. Then the simulated tests are used for comparing the overhead of the LCTR latch and the other two typical TNU self-recoverable latches. The results reveal that the LCTR latch decreases power consumption by 56.98%, delay by 30.08%, area by 21.25% and PDP by 70.77%, respectively. Moreover, the LCTR latch is moderately sensitive to the variation of process, voltage and temperature (PVT). [ABSTRACT FROM AUTHOR]

AbstractThe tension uniformity and surface accuracy of mesh antenna cable net are two well-accepted evaluation criteria for form finding. Traditional form finding techniques often rely on single-objective optimization and weighting factors. Although the Non-dominated Sorting Genetic Algorithm II (NSGA-II) can address the limitation, there are still challenges of wide distribution and insufficient precision of solutions. In the present work, a novel multi-objective optimization method based on an improved NSGA-II is introduced to conduct mesh antenna cable net form finding optimization. Based on the preference region (PR), a self-adaptive penalty function is proposed. The function is designed to steer the optimization process toward the PR, and enhancing the precision of the solutions. The extent of penalization for an individual solution depends on its positioning relative to the PR and is modified by a penalty coefficient that adapts dynamically to the population number within the PR. Additionally, a form finding method considering flexible frames is developed. The initial population for the flexible frame optimization is sourced from results of rigid frames, with the maximum truss node position error as the convergence criterion. Benchmark tests and case studies confirm that the improved NSGA-II enhances distributivity and convergence, while also providing form-finding results with higher surface accuracy and more uniform tension distribution. [ABSTRACT FROM AUTHOR]

In an endeavor to tackle the limitations of fixed collaborative scripts and to further support collaborative learning practices, adaptive collaborative scripts have been implemented within the educational domain. However, the divergent findings surrounding the efficacy of adaptive collaborative scripts engendered a compelling need for a comprehensive understanding of the adaptive scripts. This study explored the effectiveness of adaptive scripts through a systematic analysis of 11 meticulously chosen studies and demonstrated the distinct contributions offered by varying types of adaptive collaborative scripts (namely, fading-out scripts, adaptable scripts, and self-adaptive scripts). The results underscored adaptive scripts yielded a favorable impact on the caliber of collaboration and on learners’ psychological contentment. Nonetheless, the ramifications on learning outcomes were inconclusive. Under the guidance of activity theory, this review uncovered the optimal application conditions of each variant of adaptive script. The fading-out scripts necessitated supplementary consistent support to ensure satisfactory learning outcomes. Adaptable scripts demonstrated stable efficacy when employed for students with good self-regulation ability. The self-adaptive scripts yielded optimal results in learning activities with higher-level educational objectives. Distributed roles within learning groups and perceived adaptivity also influenced the efficacy of self-adaptive scripts. Implications, limitations, and future directions were also provided. [ABSTRACT FROM AUTHOR]

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid network structure to achieve more discriminant feature representations for speaker recognition, termed Resformer, local frame-level features are extracted by convolution operation, and global segment-level features are extracted by transformer, which the information learned from the frame-level features is used to guide the segment-level feature learning, to take advantage of convolutional operations and self-attention mechanisms for enhanced representation learning. Resformer uses an interactive approach to combine local features and global representations at varying resolutions. With a concurrent structure, Resformer ensures that both local features and global representations are preserved to the fullest extent possible. Extensive experiments are performed on VoxCeleb, a public SV dataset, and the findings indicate that Resformer outperforms existing deep embedding architectures by a margin of at least 10–37% on the test set. Our proposed approaches are shown to achieve significant improvement over prior methods through the ablation experiments. [ABSTRACT FROM AUTHOR]

The field of self-adaptive software is becoming increasingly crucial because software has to adapt its behavior at runtime to keep up with dynamic and constantly evolving environments. The ability of software to modify and adjust itself is known as adaptability, which has been acknowledged as an important quality attribute. As software architecture development represents the initial stage of the design process, architectural design has emerged as a critical development activity. The degree to which the architecture can adapt to changes will be the key factor in determining the adaptability of the software ultimately released to users. Existing metrics for software architectural adaptability typically view adaptability as a positive attribute, meaning that any possibility of change in architectural elements is perceived as an improvement in adaptability. However, the application of adaptability that leads to increased costs or complexity can only be seen as a negative attribute. In other words, the side effects brought about solely for the purpose of achieving adaptability are greatly overlooked; that is, the impacts caused by adaptability exhibit both symmetry and asymmetry for software architectural adaptability. On the other hand, the existing measures of adaptability only define it from the perspective of whether a module will change, without considering changes beyond the topological structure under multiple strategies. In this paper, we propose a comprehensive assessment approach to measure software architecture adaptability. Multiple factors are considered, including the number and types of strategies employed, the cost of completing the adaptation, the extent of changes made to the architectural elements, and the overall impact of the adaptation on the architecture. An extended UML (e-UML) component diagram is presented to model software architectural changes under various types of strategies. Finally, an experiment on the znn.com software is conducted to validate the practical feasibility of our proposed method. Our approach can assist software architects in making informed decisions regarding software architecture design options to improve the adaptability and longevity of their software systems. [ABSTRACT FROM AUTHOR]

Autonomous systems, such as drones and rescue robots, are increasingly used during emergencies. They deliver services and provide situational awareness that facilitate emergency management and response. To do so, they need to interact and cooperate with humans in their environment. Human behaviour is uncertain and complex, so it can be difficult to reason about it formally. In this article, we propose IDEA: an adaptive software architecture that enables cooperation between humans and autonomous systems, by leveraging the social identity approach. This approach establishes that group membership drives human behaviour. Identity and group membership are crucial during emergencies, as they influence cooperation among survivors. IDEA systems infer the social identity of surrounding humans, thereby establishing their group membership. By reasoning about groups, we limit the number of cooperation strategies the system needs to explore. IDEA systems select a strategy from the equilibrium analysis of game-theoretic models that represent interactions between group members and the IDEA system. We demonstrate our approach using a search-and-rescue scenario, in which an IDEA rescue robot optimises evacuation by collaborating with survivors. Using an empirically validated agent-based model, we show that the deployment of the IDEA system can reduce median evacuation time by 13.6%. [ABSTRACT FROM AUTHOR]

Human beings have created numerous laws, sayings and proverbs that still influence behaviors and decision-making processes of people. Some of the laws, sayings or proverbs are used by people to understand the phenomena that may take place in daily life. For example, Murphy's law states that "Anything that can go wrong will go wrong." Murphy's law is helpful for project planning with analysis and the consideration of risk. Similar to Murphy's law, the old saying "Two heads are better than one" also influences the determination of the ways for people to get jobs done effectively. Although the old saying "Two heads are better than one" has been extensively discussed in different contexts, there is a lack of studies about whether this saying is valid and can be applied in evolutionary computation. Evolutionary computation is an important optimization approach in artificial intelligence. In this paper, we attempt to study the validity of this saying in the context of evolutionary computation approach to the decision making of ridesharing systems with trust constraints. We study the validity of the saying "Two heads are better than one" by developing a series of self-adaptive evolutionary algorithms for solving the optimization problem of ridesharing systems with trust constraints based on the saying, conducting several series of experiments and comparing the effectiveness of these self-adaptive evolutionary algorithms. The new finding is that the old saying "Two heads are better than one" is valid in most cases and hence can be applied to facilitate the development of effective self-adaptive evolutionary algorithms. Our new finding paves the way for developing a better evolutionary computation approach for ridesharing recommendation systems based on sayings created by human beings or human intelligence. [ABSTRACT FROM AUTHOR]

Limited resources for edge computing platforms in airborne and spaceborne imaging payloads prevent using complex image processing models. Model pruning can eliminate redundant parameters and reduce the computational load, enhancing processing efficiency on edge computing platforms. Current challenges in model pruning for remote-sensing object detection include the risk of losing target features, particularly during sparse training and pruning, and difficulties in maintaining channel correspondence for residual structures, often resulting in retaining redundant features that compromise the balance between model size and accuracy. To address these challenges, we propose the L1 reweighted regularization (L1RR) pruning method. Leveraging dynamic and self-adaptive sparse modules, we optimize L1 sparsity regularization, preserving the model's target feature information using a feature attention loss mechanism to determine appropriate pruning ratios. Additionally, we propose a residual reconstruction procedure, which removes redundant feature channels from residual structures while maintaining the residual inference structure through output channel recombination and input channel recombination, achieving a balance between model size and accuracy. Validation on two remote-sensing datasets demonstrates significant reductions in parameters and floating point operations (FLOPs) of 77.54% and 65%, respectively, and a 48.5% increase in the inference speed on the Jetson TX2 platform. This framework optimally maintains target features and effectively distinguishes feature channel importance compared to other methods, significantly enhancing feature channel robustness for difficult targets and expanding pruning applicability to less difficult targets. [ABSTRACT FROM AUTHOR]

Most existing autoscaling approaches help to avoid violating the performance-related Service Level Objectives (SLO). However, these solutions do not aim to recover the SLO. The proposed novel autoscaling solution covers both SLO violation avoidance and recovery. The SLO failure avoidance part of the solution aims to avoid SLO violations by adjusting autoscaling thresholds based on compliance with SLO. It also dynamically selects the required CPU threshold, cooldown intervals and the number of replicas based on load velocity. The recovery part of the solution aims to recover SLO by additional resource provisioning if SLO is violated due to a delay or resource underestimation. The proposed implementation of the solution targets embarrassingly parallel workloads. It is compared with two autoscaling solutions in five workload scenarios, evaluating the ability of the solution to operate close to the defined SLO values. The results show that the proposed solution provides sufficient resources to support or recover performance-based SLOs. [ABSTRACT FROM AUTHOR]

Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users' preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context-aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems' awareness of their users' context. [ABSTRACT FROM AUTHOR]

This paper addresses the intricate challenge posed by remote handling (RH) operations in facilities with operational lifespans surpassing 30 years. The extended RH task horizon necessitates a forward-looking strategy to accommodate the continuous evolution of RH equipment. Confronted with diverse and evolving hardware interfaces, a critical requirement emerges for a flexible and adaptive software architecture based on changing situations and past experiences. The paper explores the inherent challenges associated with sustaining and upgrading RH equipment within an extended operational context. In response to this challenge, a groundbreaking, flexible, and maintainable human–machine interface (HMI) architecture named MAMIC is designed, guaranteeing seamless integration with a diverse range of RH equipment developed over the years. Embracing a modular and extensible design, the MAMIC architecture facilitates the effortless incorporation of new equipment without compromising system integrity. Moreover, by adopting this approach, nuclear facilities can proactively steer the evolution of RH equipment, guaranteeing sustained performance and compliance throughout the extended operational lifecycle. The proposed adaptive architecture provides a scalable and future-proof solution, addressing the dynamic landscape of remote handling technology for decades. [ABSTRACT FROM AUTHOR]

Show-through phenomena have always been a challenging issue in color-document image processing, which is widely used in various fields such as finance, education, and administration. Existing methods for processing color-document images face challenges, including dealing with double-sided documents with show-through effects, accurately distinguishing between foreground and show-through parts, and addressing the issue of insufficient real image data for supervised training. To overcome these challenges, this paper proposes a self-supervised-learning-based method for removing show-through effects in color-document images. The proposed method utilizes a two-stage-structured show-through-removal network that incorporates a double-cycle consistency loss and a pseudo-similarity loss to effectively constrain the process of show-through removal. Moreover, we constructed two datasets consisting of different show-through mixing ratios and conducted extensive experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art methods and can effectively perform show-through removal without the need for paired datasets. Specifically, the proposed method achieves an average PSNR of 33.85 dB on our datasets, outperforming comparable methods by a margin of 0.89 dB. [ABSTRACT FROM AUTHOR]

Adaptive Mobile Learning Systems (AMLSs) are technological tools that incorporate adaptive learning to generate an individual teaching–learning process for each student. Despite the proliferation of AMLS, their development is often hindered by a lack of standardization and the poor quality of existing software architectures in terms of requirements correctness and coverage. This paper presents a systematic literature review (SLR) of software architectures oriented to developing AMLS reported in the state of the art. For this SLR, we searched the ACM Digital Library, IEEE Xplore, Science Direct, Springer, and SCOPUS. Studies published in English that describe and evaluate a software architecture to develop AMLS were included. Twenty-two primary works were relevant to the present study. Based on the obtained results, we have identified key aspects that must be considered in a software architecture proposed for developing an AMLS in terms of requirements, quality attributes, stakeholders, services provided to users, views and architectural styles, components, and implementation technologies. The main finding of this work is the lack of a reference software architecture that considers all the requirements and quality attributes needed for developing AMLS. This gap hinders the effective development and standardization of quality AMLSs, suggesting a critical need for a comprehensive reference software architecture. [ABSTRACT FROM AUTHOR]

For solving a broad class of nonconvex programming problems on an unbounded constraint set, we provide a self-adaptive step-size strategy that does not include line-search techniques and establishes the convergence of a generic approach under mild assumptions. Specifically, the objective function may not satisfy the convexity condition. Unlike descent line-search algorithms, it does not need a known Lipschitz constant to figure out how big the first step should be. The crucial feature of this process is the steady reduction of the step size until a certain condition is fulfilled. In particular, it can provide a new gradient projection approach to optimization problems with an unbounded constrained set. To demonstrate the effectiveness of the proposed technique for large-scale problems, we apply it to some experiments on machine learning, such as supervised feature selection, multi-variable logistic regressions and neural networks for classification. [ABSTRACT FROM AUTHOR]

When resource demand increases and decreases rapidly, container clusters in the cloud environment need to respond to the number of containers in a timely manner to ensure service quality. Resource load prediction is a prominent challenge issue with the widespread adoption of cloud computing. A novel cloud computing load prediction method has been proposed, the Double-channel residual Self-attention Temporal convolutional Network with Weight adaptive updating (DSTNW), in order to make the response of the container cluster more rapid and accurate. A Double-channel Temporal Convolution Network model (DTN) has been developed to capture long-term sequence dependencies and enhance feature extraction capabilities when the model handles long load sequences. Double-channel dilated causal convolution has been adopted to replace the single-channel dilated causal convolution in the DTN. A residual temporal self-attention mechanism (SM) has been proposed to improve the performance of the network and focus on features with significant contributions from the DTN. DTN and SM jointly constitute a dual-channel residual self-attention temporal convolutional network (DSTN). In addition, by evaluating the accuracy aspects of single and stacked DSTNs, an adaptive weight strategy has been proposed to assign corresponding weights for the single and stacked DSTNs, respectively. The experimental results highlight that the developed method has outstanding prediction performance for cloud computing in comparison with some state-of-the-art methods. The proposed method achieved an average improvement of 24.16% and 30.48% on the Container dataset and Google dataset, respectively. [ABSTRACT FROM AUTHOR]

For the problem that traditional droop control cannot maintain the maximum output power of photovoltaic (PV) units, this work proposes a self‐adaptive communication‐free control scheme for the islanded PV‐storage AC microgrids. The proposed control enables the maximum power point tracking‐based output active powers of PVs by adaptively adjusting P‐f/Q‐V droop coefficients. It also facilitates adaptive allocations of reactive powers based on the available capacities of PVs and storage modules. The key characteristics of the proposed control strategy are summarized as follows: 1) PV units are controlled as voltage sources, which could participate in the voltage/frequency regulation to a certain extent; 2) maximum power utilization of PVs is obtained; 3) adaptive allocations of reactive powers are realized based on the maximum available capacity of PVs and storage modules. Subsequently, the stability analysis of the proposed self‐adaptive communication‐free control strategy is verified. Finally, the validity of the proposed self‐adaptive communication‐free control method is validated through simulations conducted using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

Featured Application: This paper proposes a method that can increase the connectivity of the knowledge graph when it is constructed and improve usability, e.g., retrieving more knowledge at the time of query. Extracting relation quintuple and feature words from unstructured text is a prelude to the construction of the scientific knowledge base. At present, the prior works use explicit clues between entities to study this task but ignore the use and the association of the feature words. In this work, we propose a new method to generate self-adaptive feature words from the original text for every single sample. These words can add additional correlation information to the knowledge graph. We allow the model to generate a new word representation and apply it to the original sentence to judge the relation type and locate the head and tail of the relation quintuple. Compared with the previous works, the feature words increase the flexibility of relying on information and improve the explanatory ability. Extensive experiments on scientific field datasets illustrate that the self-adaptive feature words method (SAFW) is good at ferreting out the unique feature words and obtaining the core part for the quintuple. It achieves good performance on four public datasets and obtains a markable performance improvement compared with other baselines. [ABSTRACT FROM AUTHOR]

In recent years, self-supervised learning-based models have been widely used for electrocardiogram (ECG) representation learning. However, most of the models utilize contrastive learning that strongly depend on data augmentation. In this paper, we propose a masked self-supervised learning model based on multiview information bottleneck principle. Our method masks the ECG signal instances in the time and frequency domains at a high ratio and then uses the autoencoder to reconstruct the original input. Not only the intra-view relations within each view but also the inter-view relations between two views are exploited in ECG representation learning. Furthermore, we use the multiview information bottleneck principle to remove redundant information in the time and frequency domains, so that the representations of both views contain more task-relevant information. Our model is pre-trained on three larger ECG datasets at once and fine-tuned on each classification task. Experimental results show that our model not only outperforms state-of-the-art models with self-supervised learning, but also outperforms models with supervised learning. [ABSTRACT FROM AUTHOR]

Software maintenance and evolution play an important role in the software engineering field, especially when current software becomes more and more complex and powerful. As an entity to implement business processes and gain revenue, valuable software is composed of business logic and corresponding organization role interaction interfaces. With the enterprise development, the organization architecture also evolves, like expanding, cross department cooperation, and so on. However, existing software process adaptive approaches mainly focus on handling the change of the business (program) logic instead of organization structure. Therefore, we propose an adaptive software business process approach that supports organization architecture evolution and automatically migrates the run‐time process instances to the latest version. First, a business process adaptation model is designed, which includes the organization layer, business process layer and event layer that connects the two. Based on the model, the organization changing impact and business process model modification are formalized. Besides, the business process adaptation approach is designed. According to the dependence between the organization architecture and the business process activities, the affected domain detection algorithms for three basic business process structures and the business process instance migration algorithm are developed. Finally, the feasibility and stability of the proposed system are comprehensively evaluated with the synthetic data sets. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) need to replicate feature detectors when modeling spatial information, which reduces their efficiency. The number of replicated feature detectors or labeled training data required for such methods grows exponentially with the dimensionality of the data being used. On the other hand, space-insensitive methods are difficult to encode and express effectively due to the limitation of their rich text structures. In response to the above problems, this paper proposes a capsule network (self-attention capsule network, or SA-CapsNet) with a self-attention mechanism for text classification tasks, wherein the capsule network itself, given the feature with the symmetry hint on two ends, acts as both encoder and decoder. In order to learn long-distance dependent features in sentences and encode text information more efficiently, SA-CapsNet maps the self-attention module to the feature extraction layer of the capsule network, thereby increasing its feature extraction ability and overcoming the limitations of convolutional neural networks. In addition, in this study, in order to improve the accuracy of the model, the capsule was improved by reducing its dimension and an intermediate layer was added, enabling the model to obtain more expressive instantiation features in a given sentence. Finally, experiments were carried out on three general datasets of different sizes, namely the IMDB, MPQA, and MR datasets. The accuracy of the model on these three datasets was 84.72%, 80.31%, and 75.38%, respectively. Furthermore, compared with the benchmark algorithm, the model's performance on these datasets was promising, with an increase in accuracy of 1.08%, 0.39%, and 1.43%, respectively. This study focused on reducing the parameters of the model for various applications, such as edge and mobile applications. The experimental results show that the accuracy is still not apparently decreased by the reduced parameters. The experimental results therefore verify the effective performance of the proposed SA-CapsNet model. [ABSTRACT FROM AUTHOR]

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Temperature management in modern instruments is often a great task, particularly for silicon chip technologies against the background of the ever-increasing demanding for larger scale and higher density electronics integration. Enormous efforts have been made to solve this long-pending issue, mostly relying on active equipment that consume more energy and more space. Here, a compact thermal management technique for silicon chips is proposed, which is able to passively maintain the operation temperature of targets within a wide range of input power. The core part is a self-adaptive near-field thermal radiation system made of a phase-changeable metasurface and graphene/hBN heterostructure with surface plasmon/phonon modes. Numerically, we show that integrated with such a setup, a 0.1-mm thick silicon substrate could automatically maintain its operation temperature within a narrow window (∼333 ± 7 K) when loaded with heat power varied in 0.1–1 W cm−2. As a comparison, the temperature will change 614 or 319 K for a bare or blackbody-coated silicon substrate. The dynamic process of thermal homeostasis is discussed by using the transient thermal equation. The results imply that the current design is suitable for providing a compact, conformal thermal functional coat to passively manage temperatures of heated electronic components, particularly in vacuum. [ABSTRACT FROM AUTHOR]

The development of Self-adaptive Software (SaS) is not a trivial task because this type of software has specific features compared to traditional ones. In short, SaS can reflect on its internal and external states and propose structural, behavioral, and contextual changes that can be incorporated at runtime. Manual adaptation tasks, even if very well executed, normally become onerous in time and effort, besides being error prone because of the involuntary injection of errors by the developers. Automated processes have been used as a feasible solution to conduct software adaptation at runtime by minimizing human involvement (e.g., software engineers and developers) and quickening up the execution of tasks. In parallel, Reference Architectures (RA) have been used to aggregate knowledge and architectural artifacts, capturing the systems’ essence in specific domains. Therefore, it can be said that this type of architecture is an important way to support the development, standardization, and evolution of software systems. Considering this context, the main contribution of this paper is to present the second release of a reference architecture called RA4SaS (Reference Architecture for SaS). This architecture is based on reflection, a controlled adaptation approach, and a set of automated processes that support the development of SaS in both design and runtime. To show the applicability of our RA, we conducted a case study that explored three adaptation scenarios. As a result, we observe our RA has good potential to efficiently contribute to the SaS domain.

In this work, an adaptive software architecture is proposed for the generation of experiences for hotel promotion and marketing, based on Case-based Reasoning (CBR) that uses the attributes and user characteristics and immersive 360° videos. Considering that immersion in virtual reality (VR) environments can trigger responses in various dimensions, such as affective, cognitive, attitudinal, and behavioral dimensions, these dimensions are evaluated in immersive environments with 360° videos. To validate the results obtained with the software architecture, a quasi-experimental study was conducted through the evaluation of the experience, consisting in the visualization of the environments of a boutique hotel, with a sample of a randomly selected group of young people. The contribution of this work lies in the use of 360° VR videos, for the visualization of the hotel characteristics and environments according the user profiles, to evaluate the affective, cognitive and attitudinal and behavioral responses and their influence on the booking intention and attitude. Finally, conclusions and recommendations for future work have been established. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is recognized for its role in enhancing the performance of learning algorithms, especially for high-dimensional datasets. In recent times, FS has been framed as a multi-objective optimization problem, leading to the application of various multi-objective evolutionary algorithms (MOEAs) to address it. However, the solution space expands exponentially with the dataset's dimensionality. Simultaneously, the extensive search space often results in numerous local optimal solutions due to a large proportion of unrelated and redundant features [H. Adeli and H. S. Park, Fully automated design of super-high-rise building structures by a hybrid ai model on a massively parallel machine, AI Mag. 17 (1996) 87–93]. Consequently, existing MOEAs struggle with local optima stagnation, particularly in large-scale multi-objective FS problems (LSMOFSPs). Different LSMOFSPs generally exhibit unique characteristics, yet most existing MOEAs rely on a single candidate solution generation strategy (CSGS), which may be less efficient for diverse LSMOFSPs [H. S. Park and H. Adeli, Distributed neural dynamics algorithms for optimization of large steel structures, J. Struct. Eng. ASCE 123 (1997) 880–888; M. Aldwaik and H. Adeli, Advances in optimization of highrise building structures, Struct. Multidiscip. Optim. 50 (2014) 899–919; E. G. González, J. R. Villar, Q. Tan, J. Sedano and C. Chira, An efficient multi-robot path planning solution using a* and coevolutionary algorithms, Integr. Comput. Aided Eng. 30 (2022) 41–52]. Moreover, selecting an appropriate MOEA and determining its corresponding parameter values for a specified LSMOFSP is time-consuming. To address these challenges, a multi-objective self-adaptive particle swarm optimization (MOSaPSO) algorithm is proposed, combined with a rapid nondominated sorting approach. MOSaPSO employs a self-adaptive mechanism, along with five modified efficient CSGSs, to generate new solutions. Experiments were conducted on ten datasets, and the results demonstrate that the number of features is effectively reduced by MOSaPSO while lowering the classification error rate. Furthermore, superior performance is observed in comparison to its counterparts on both the training and test sets, with advantages becoming increasingly evident as the dimensionality increases. [ABSTRACT FROM AUTHOR]

Digital modelling of Chinese Ancient Timber Architecture (CATA) presents a favorable approach towards preserving ancient structures. However, the complex nature of CATA poses a significant challenge in generating digital models. The present research endeavors to identify the Greater Carpentry Structure (GCS) as the attribute framework of CATA. In doing so, a structured metamodel approach is employed to divide GCS into two categories of primitive entities, namely bays and components, and establish the topological metamodel that depicts the relationships between the two. The proposed self-adaptive modelling method utilizes bay carpentry as its focal point to perform horizontal and vertical incorporation of the components based on the developed topological metamodel. This approach facilitates the automatic generation of intricate models of CATA. The application of this methodology exhibits promising results for two typical GCS structures, displaying accuracy, efficiency, flexibility, and scalability in accomplishing self-adaptive reconstruction from one structure type to another. [ABSTRACT FROM AUTHOR]

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process. [ABSTRACT FROM AUTHOR]

Adaptive User Interface (AUI) can change its layout, appearance, and/or elements based on the needs of its user requirements and current usage context. The AUIs are used in state-of-the-art software products, applications for mobile devices, and websites. Moreover, AUI is an emerging research field in a mobile context, as it can enhance usability, performance, and user satisfaction. This study aims to propose a conceptual framework for developing a real-time self-adaptive user interface based on the Android Operating System (OS). Furthermore, the focus is on developing the core algorithms for the modules of the proposed framework. To evaluate the performance of the proposed framework, three case studies have been designed based on the daily and weekly activities of the user. Moreover, an expert-based validation approach is employed to obtain the expert's feedback regarding the proposed framework. The result indicates that the proposed framework helps improve user satisfaction and experience by making an intelligent mobile device interface. The results of the framework's evaluation and validation show the proposed framework's feasibility and effectiveness. We conclude that the current work is beneficial in filling the identified research gap. Moreover, this research shows the significance of an adaptive interface in an Android OS-based context. In addition, it not only helps in improving the user interest and satisfaction but also enhances the overall performance of the mobile device. [ABSTRACT FROM AUTHOR]

Systems using graphical processing units (GPUs) and field-programmable gate arrays (FPGAs) have increased due to their advantages over central processing units (CPUs). However, such systems require the understanding of hardware-specific technical specifications such as Hardware Description Language (HDL) and compute unified device architecture (CUDA), which is a high hurdle. Based on this background, we previously proposed environment-adaptive software that enables automatic conversion, configuration and high-performance operation of existing code according to the hardware to be placed. As an element of this concept, we also proposed a method of automatically offloading loop statements of application source code for CPUs to GPUs and FPGAs. In this paper, we propose a method for offloading a function block, which is a larger unit, instead of individual loop statements in an application to achieve higher speed by automatically offloading to GPUs and FPGAs. We implemented the proposed method and evaluated it using current applications offloading to GPUs and FPGAs. [ABSTRACT FROM AUTHOR]

The modern development approaches are establishing microservices and cloud computing as major trends to benefit the technological community. However, these technologies are often prone to multiple issues regarding parallel development by numerous parties, delivery strategies and resource allocation. This paper proposes a novel architecture for developing self-adaptive microservices, using Kubernetes through the Azure Container Apps, including a strategy that will complement the architecture to enhance the development of microservices and aiming to achieve a solution that allows the readers to deliver software faster, with more resilience, more scalable, and more cost-effective, depending as low as possible from human intervention to maintain and scale. The author will apply the acquired knowledge to propose and test an architecture for a real use case scenario, building a notifications service integrated with a complex cloud-based web application system. [ABSTRACT FROM AUTHOR]

Naked mole-rat algorithm (NMRA) is a swarm intelligence-based algorithm that draws inspiration from the mating behaviour of mole rats (workers and breeders). This approach, which is based on the ability of breeders to reproduce with the queen, has been utilized to tackle optimization problems. The algorithm, however, suffers from local optima stagnation problem and a slower rate of convergence in order to provide gobal optimal solution. This study suggests attraction and repulsion strategy based NMRA (ARNMRA) along with self-adaptive properties to avoid trapping of solution in local optima. This strategy is utilized to create new breeder rat solutions and mating factor (λ) is made self-adaptive using simulated annealing (sa) based mutation operator. ARNMRA is evaluated on CEC 2005 numerical benchmark problems and found to be superior to other algorithms, including well-known ones like selective operation based GWO (SOGWO), opposition based laplacian equilibrium optimizer (OB-L-EO), improved whale optimization algorithm (IWOA), success-history based adaptive DE (SHADE) and original NMRA. Further, according to experimental results, the performance of ARNMRA is likewise superior to the NMRA for the CEC 2019 and CEC 2020 numerical problems. Convergence profiles and statistical tests (rank-sum test and Friedman test) are employed further to validate the experimental results. Moreover, this article extends the application of ARNMRA to address the data gathering aspect in mobile wireless sensor networks (MWSNs) with the goal of prolonging network lifetime and enhancing energy efficiency. In this MWSN-based protocol, a sensor node is elected as a cluster head based on factors like mobility, residual energy, and connection time. The protocol aims to maximize the system lifetime by efficiently collecting data from all sensors and transmitting it to the base station. The study emphasizes the significance of considering dynamic node densities and speed when designing effective data-gathering protocols for MWSNs. [ABSTRACT FROM AUTHOR]

Conventional liquid cooling techniques may provide effective chip cooling but at the expense of high pumping power consumption. Considering that there is dynamic heat load in practice, a self-adaptive cooling technique is desired to reduce operational costs while preserving inherent cooling effectiveness. In this work, a novel self-adaptive cooling strategy is presented to balance the thermal and flow efficiency in accordance with the dynamic thermal load, based on temperature-regulated movement of the metal pillar array in a microfluidic channel. With an illustrative device, the effectiveness of such a strategy is investigated using multiphysics modeling and simulation. As a case study, the device is considered to be initiated with a chip power of 5 W and an inlet coolant velocity of 0.3 m/s. It is shown that the temperature-regulated movement of the metal pillar heat sink will be activated rapidly and equilibrate within 30 s. Parts of the metal pillars immerse into the coolant flow, resulting in significantly improved heat transfer efficiency. The diminished thermal resistance leads to a reduction in chip temperature rise from 225 K (without structural adaptation) to 91.86 K (with structural adaption). Meanwhile, the immersion of metal pillars into the coolant also causes an increased flow resistance in the microfluidic channel (i.e., pressure drop increases from 859.27 Pa to 915.98 Pa). Nevertheless, the flow resistance decreases spontaneously when the working power of the chip decreases. Comprehensive simulation has demonstrated that the temperature-regulated structure works well under various conditions. Therefore, it is believed that the presented self-adaptive cooling strategy offers simple and cost-effective thermal management for modern electronics with dynamic heat fluxes. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new three-step iterative scheme for finding the common solutions of the variational inequality using the technique of updating the solution. We suggest, iterative algorithms involving three-steps for the predictor-corrector method of variational inequality in real Hilbert spaces H. Our results include the Takahashi and Toyoda, extra gradient, Mann and Noor iterations as special cases. We also investigate the convergence criteria of the three-step iterative scheme. As special cases, the earlier findings are included in our results, which can be seen as an advancement and improvement over the previous investigation. This is a new refinement in our existing literature and previously known algorithms. A numerical example is given to illustrate the efficiency and performance of the proposed self-adaptive scheme. [ABSTRACT FROM AUTHOR]

In this study, innovative, intelligent software based on Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to effectively estimate the corn drying process parameters using data from getting experimental study. For this purpose, laboratory-scale corn drier experimental setup was established to get corn drying parameters (air temperature in the different locations, air humidity, and capacitance value). The twelve sensors measure the drying parameters (s1, s2, s3, ..., s12) such as inlet air temperature T1 with ±0.5 °C sensitivity, relative humidity RH1 with ±0.1 RH, inlet temperatures T2 and Tw2 with the ±0.5 °C, dryer chamber temperature Ti (i = 3,...,7) with the ±0.5 °C, outlet temperatures T8 and Tw8 with the ±0.5 °C, product moisture content with ±0.05 gr. Finally, depending on the twelve sensor parameters, the capacitance value was measured using a capacitive sensor with ±0.05% sensitivity. Accordingly, an Adaptive Neuro-Fuzzy Inference System architecture was developed with twelve inputs and one output. For the training of the Adaptive Neuro-Fuzzy Inference System, 52 data sets from laboratory studies were used, and Adaptive Neuro-Fuzzy Inference System was successfully trained with absolute fraction of variance 0.998 and mean squared error 0.8007. For the testing the performance of the Adaptive Neuro-Fuzzy Inference System 51 dataset was used and testing performance of the ANFIS getting with the absolute fraction of variance 0.9963 and mean squared error 1.47. Finally, the performance of the Adaptive Neuro-Fuzzy Inference System compared with the artificial neural network (ANN), and it is clearly seen that the performance of the fuzzy-neuro hybrid algorithm is higher than the core neuro algorithm with %0.97 for the training and %31.5 for the testing. By forecasting the moisture content of corn under various operating conditions, the Adaptive Neuro-Fuzzy Inference System model can be used to improve the operation of the corn dryer, resulting in increased efficiency, lower energy usage, and higher-quality dried corn. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have been used extensively for the optimal design of water distribution networks (WDNs). There is evidence in the literature that search space reduction is highly effective. However, practical methods that do not introduce extra computational requirements are lacking. A dynamic search space reduction methodology is proposed to search the entire solution space without eliminating any part of the search space beforehand. The proposed methodology works on the information explored during the execution of the algorithm. Further, a self-adaptive penalty is suggested which is based on both flow and pressure deficits instead of only pressure deficit and is obtained using pressure dependent analysis. In this study, the methodology is demonstrated using a Genetic Algorithm (GA). The effectiveness of the methodology is demonstrated on the Ramnagar Network of Nagpur City, India and two benchmark problems from the literature. The proposed methodology resulted in a substantial reduction in the computational efforts and provided nine improved solutions as compared to the best solution available in the literature for one of the networks. The techniques proposed are generic and can be incorporated in other EAs. [ABSTRACT FROM AUTHOR]

Magnetic soft robots have the potential to directly access areas of the human body that are currently inaccessible or difficult to reach for minimally invasive medical operations or drug delivery. Recently, ferrofluids containing magnetic nanoparticles with unique deformation properties have been extensively investigated. In this study, a ferrofluid robot for targeted drug delivery is proposed, while the control of ferrofluid robots is implemented in a 3D vascular model. Four-solenoid control systems with visual feedback are designed to achieve said implementation. Through numerical simulation and actual measurement, the magnetic field gradient generated by the system can reach 4.14 T/m, and three-dimensional control of ferrofluid robots can be realized. The deformation ability of ferrofluid robots is investigated, and the ferrofluid robots are found to pass through slits four times smaller than the diameter of the ferrofluid robots. Also, the deformation of the ferrofluid robots can reduce the drag force in the flow field by 43.75% through finite element simulation. The maximum control error does not exceed 0.3 mm. Finally, the coil system controls a ferrofluid droplet to achieve autonomous motion in a 3D vascular model with a maximum internal diameter of 3 mm. The implementation of the control effect allows for the technical potential of ferrofluid robots in drug delivery through vascular navigation. [ABSTRACT FROM AUTHOR]

The self-adaptive multi-agent system requires adaptive adjustments based on the dynamic environment during its runtime. Heterogeneous agent can accomplish different task goals, enhance the efficiency of system operation, but its complex collaboration problem poses new challenges to the study of verification of adaptive policies for heterogeneous multi-agents. This paper proposes a runtime verification method for self-adaptive multi-agent systems using probabilistic timed automata. The method constructs a probabilistic timed automaton model by formally describing the functional characteristics of heterogeneous agents and integrating random factors in the environment to simulate the operation process of the self-adaptive multi-agent system. Regarding the collaboration logic among heterogeneous agents, security constraints are established to ensure the security of state transition processes during system operation. Combining model checking with runtime quantitative verification methods to conduct experiment and applying it in the case of an intelligent unmanned parking system. Experimental results manifest the correctness of the cooperation logic between agents can effectively ensure the stability of the system at runtime. Significant improvement in system uptime and efficiency compared to the initial system without runtime quantitative validation. [ABSTRACT FROM AUTHOR]

In this paper, by combining the inertial technique and the gradient descent method with Polyak's stepsizes, we propose a novel inertial self-adaptive gradient algorithm to solve the split feasibility problem in Hilbert spaces and prove some strong and weak convergence theorems of our method under standard assumptions. We examine the performance of our method on the sparse recovery problem beside an example in an infinite dimensional Hilbert space with synthetic data and give some numerical results to show the potential applicability of the proposed method and comparisons with related methods emphasize it further. [ABSTRACT FROM AUTHOR]

A large volume of network trace data are collected by the government and public and private organizations and can be analyzed for various purposes such as resolving network problems, improving network performance, and understanding user behavior. However, most organizations are reluctant to share their data with any external experts for analysis, because they contain sensitive information deemed proprietary to the organization, thus raising privacy concerns. Even if the payload of network packets is not shared, header data may disclose sensitive information that adversaries can exploit to perform unauthorized actions. So network trace data need to be anonymized before being shared. Most of the existing anonymization tools have two major shortcomings: (1) they cannot provide provable protection, and (2) their performance relies on setting the right parameter values such as the degree of privacy protection and the features that should be anonymized, but there is little assistance for a user to optimally set these parameters. This article proposes a self-adaptive and secure approach to anonymize network trace data and provides provable protection and automatic optimal settings of parameters. A comparison of the proposed approach with existing anonymization tools via experimentation demonstrated that the proposed method outperforms the existing anonymization techniques. [ABSTRACT FROM AUTHOR]

While originally designed for natural language processing tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision: (1) treating images as 1D sequences neglects their 2D structures; (2) the quadratic complexity is too expensive for high-resolution images; (3) it only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel linear attention named large kernel attention (LKA) to enable self-adaptive and long-range correlations in self-attention while avoiding its shortcomings. Furthermore, we present a neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple, VAN achieves comparable results with similar size convolutional neural networks (CNNs) and vision transformers (ViTs) in various tasks, including image classification, object detection, semantic segmentation, panoptic segmentation, pose estimation, etc. For example, VAN-B6 achieves 87.8% accuracy on ImageNet benchmark, and sets new state-of-the-art performance (58.2 PQ) for panoptic segmentation. Besides, VAN-B2 surpasses Swin-T 4 mIoU (50.1 vs. 46.1) for semantic segmentation on ADE20K benchmark, 2.6 AP (48.8 vs. 46.2) for object detection on COCO dataset. It provides a novel method and a simple yet strong baseline for the community. The code is available at https://github.com/Visual-Attention-Network. [ABSTRACT FROM AUTHOR]

From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches. [ABSTRACT FROM AUTHOR]

Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devices. Self-occlusions and non-line of sight (NLOS) situations are important representatives among them. To address these challenges, this paper presents a novel system utilizing dual Kinect V2, enhanced by an advanced Transmission Control Protocol (TCP) and sophisticated ensemble learning techniques, tailor-made to handle self-occlusions and NLOS situations. Our main works are as follows: (1) a data-adaptive adjustment mechanism, anchored on localization outcomes, to mitigate self-occlusion in dynamic orientations; (2) the adoption of sophisticated ensemble learning techniques, including a Chirp acoustic signal identification method, based on an optimized fuzzy c-means-AdaBoost algorithm, for improving positioning accuracy in NLOS contexts; and (3) an amalgamation of the Random Forest model and bat algorithm, providing innovative action identification strategies for intricate scenarios. We conduct extensive experiments, and our results show that the proposed system augments human action recognition precision by a substantial 30.25%, surpassing the benchmarks set by current state-of-the-art works. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment. [ABSTRACT FROM AUTHOR]

The relevant problem of analyzing the features of measurement systems and their metrological support is formulated. Promising areas for the development of measurement systems, including virtual systems, are considered. The article analyzes such development areas as remote and synchronized vector measurements, cloud technologies, the Internet of Things, big data, and artificial intelligence. Several innovative solutions employing both biosensors and hybrid metrology are described (introduction of structural redundancy into measurement systems, as well as the introduction of temporal and algorithmic redundancies into the software of measurement systems). It is shown that the integration of component functions objectively complicates the identification of measurement systems in complex technical systems, yet providing new possibilities for technical systems with measurement functions. The following areas for improving the metrological support of measurement systems are analyzed: self-diagnostics and self-monitoring; remote and automated calibration and verification; use of digital twins, big data, and artificial intelligence; establishment of adaptive calibration and verification intervals. The study reveals the need to minimize bureaucratic procedures and automate metrological procedures while showing practicability in switching from periodic to adaptive procedures requiring no human involvement. [ABSTRACT FROM AUTHOR]