Reviewerly/exHarmony huggingface dataset:
MNEARO: A meta swarm intelligence optimization algorithm for engineering applications

Artificial rabbits (AR) optimization is a newly proposed swarm intelligence algorithm. The idea of AR optimization (ARO) is inspired by the nature behaviors of the AR including the detour foraging, the random hiding and the energy shrink. Although ARO has better performance than many classical natural heuristic algorithms, it still has some disadvantages such as lacking the diversity of the population and easy to trap in the local optimal. To mitigate the influence of the above defects, this paper respectively introduces the mutation strategy, the prey identification strategy and the elite opposition-based learning strategy into the original algorithm, and proposes a new meta swarm intelligence optimization algorithm termed MNEARO. Firstly, the mutation strategy is introduced into the two natural behaviors of ARO to expand its exploration range in the solution space and increase the probability of finding the global optimal. Secondly, replacing the captured individuals with the predators that take better action trajectories to achieve the population evolution. Another candidate population is generated by the prey identification strategy, thus improving the population diversity and dynamics of ARO. Finally, the elite opposition-based learning strategy improves the exploitation ability of ARO and the overall quality of the population by selecting some individuals with the better fitness values as the elites and using the greedy selection before the end of each iteration. A series of performance tests are conducted on the CEC2020 test set in four dimensions and the CEC2022 test set in the highest dimension respectively to verify that the proposed MNEARO has better convergence and robustness. In addition, the practicality of MNEARO is further highlighted through five mechanical optimization designs, two truss topology optimization designs and the vehicle cruise control system. The experimental results show that the proposed MNEARO has a strong competitiveness in solving complex optimization problems with different dimensions.
Modeling Awareness Requirements in Groupware: From Cards to Diagrams

Up to now, groupware has enjoyed a certain stability in terms of the users' technical requirements, being the awareness dimension one of its key services to provide usability and improve collaboration.Nonetheless, currently, groupware technologies are being stressed: on the one hand, the pandemic of COVID-19 has greatly driven the massive use of groupware tools to overcome physical distancing; on the other hand, the new digital worlds (with disruptive devices, changing paradigms, and growing productive needs) are introducing new collaboration settings.This, and the fact that software engineering methods are not paying enough attention to the awareness, makes us concentrate on facilitating its design.Thus, we have created a visual modeling technique, based on a conceptual framework, to be used by the developers of groupware systems to describe awareness requirements.This visual language, called the awareness description diagrams, has been validated in some experimental activities.The results obtained show that this is a valid technique in order to model the awareness support, that it is useful and understandable for groupware engineers, and that the visual representation is preferred to a more textual one in terms of expressiveness.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si240.svg" display="inline" id="d1e223"><mml:mi mathvariant="normal">SimplMM</mml:mi></mml:math>: A simplified and abstract multicore hardware model for large scale system software formal verification

This paper introduces SimplMM, a novel subsystem within the Certified Concurrent Abstraction Layers (CCAL) modular software verification framework, designed specifically for fine-grained concurrent software. SimplMM aims to provide a generic, practical, and realistic multicore machine model for verifying software within the CCAL framework. While formal multicore hardware semantics have seen extensive development, their integration with large-scale software verification has received limited attention. To address this gap, we propose a novel approach: a toolkit comprising a generic sequentially consistent multicore semantics, contextual refinement templates, and libraries. These components establish crucial connections between the machine model and verified program modules (layers) using CCAL. We demonstrate the practicality of our framework by successfully integrating it with existing large-scale proofs, specifically for CertiKOS running on top of the x86 hardware architecture. This research significantly advances the field of accurate and efficient concurrent software verification and development tools for multicore systems. Our provision of a practical and formal multicore machine model, seamlessly integrated within the CCAL framework, equips developers with a powerful toolkit for large-scale concurrent software verification. The effectiveness of our approach, validated through successful integration with existing large-scale proofs such as CertiKOS, establishes a robust foundation for the design and verification of concurrent software in multicore systems.
WinTA: An Efficient Reconfigurable CNN Training Accelerator With Decomposition Winograd

Convolutional neural networks (CNNs) are expected to bridge the domain shift between the training data and real-world tasks. Moreover, the efficient training of CNNs on resource-constrained platforms has become more important because of communication latency and privacy concerns. However, deploying CNN training on edge devices is challenging due to the intensive computation and diverse computational patterns. In this work, we firstly propose a hybrid decomposition Winograd (HDW) method that significantly reduces the number of multiplications and flexibly handles various convolution operations during training. Secondly, we design a reconfigurable CNN training accelerator, named WinTA, utilizing a set of unified transformation units to support various Winograd operations. Thirdly, we implement an efficient and flexible data access scheme using a hierarchical barrel shifter network (HBSN). Experimental results on the Xilinx Alveo U50 FPGA Card demonstrate that WinTA effectively accelerates CNN training. Compared to CPU and GPU implementations, WinTA achieves speedups of 7.1 texttimes and 1.65 texttimes, respectively, while improving energy efficiency by 26.6 texttimes and 10.4 texttimes, respectively. Additionally, our design provides 1.24 texttimes and 2.04 texttimes improvements in terms of throughput and resource efficiency compared to prior-art FPGA-based training accelerator.
CBD2023: A Hypercomplex Bangla Handwriting Character Recognition Data for Hierarchical Class Expansion

Object recognition technology has made significant strides, but recognizing handwritten Bangla characters (including symbols, compound forms, etc.) remains a challenging problem due to the prevalence of cursive writing and many ambiguous characters. The complexity and variability of the Bangla script and individual's unique handwriting styles make it difficult to achieve satisfactory performance for practical applications, and the best existing recognizers are far less effective than those developed for English alpha-numeric characters. Compared to other major languages, there are limited options for recognizing handwritten Bangla characters. This research has described a new dataset to improve the accuracy and effectiveness of handwriting recognition systems for the Bengali language spoken by over 200 million people worldwide. This dataset aims to investigate and recognize Bangla handwritten characters, focusing on enlarging the recognized character classes. To achieve this, a new challenging dataset for handwriting recognition is introduced, collected from numerous students' handwriting from two institutions.
Role of boiling for the formation of vertical metal zoning and bonanza zone in low-sulfidation epithermal gold‑silver deposits: A geochemical modeling approach

Boiling and mixing are thought to be the most important processes for mineralization in the low-sulfidation epithermal gold‑silver deposits. Some low-sulfidation epithermal gold‑silver deposits show vertical zoning of metals including gold, silver, copper, lead, and zinc, but effect of boiling and mixing for the vertical zoning of metals through mineralization has been scarcely debated. Therefore, we applied geochemical modeling for an open-isenthalpic boiling system, with a hydrothermal fluid with different initial temperatures, carbon dioxide contents, salinities, and total sulfur contents to numerically evaluate the effect of boiling phenomenon for the vertical zoning in low-sulfidation epithermal gold‑silver deposits. Geochemical modeling of boiling phenomenon suggests that boiling is effective for depositing gold, silver, copper, lead, and zinc and that it could form a bonanza zone enriched in gold and silver in the shallow zone above the lead and zinc-rich zone. Temperature, carbon dioxide content, salinity, and hydrogen sulfide content affect the deposited amount of metals, vertical extent, depth of mineralization, and gold/silver ratio of the ore zone, although other factors such as permeability, gangue mineralogy, duration of mineralization, kinetics of mineral precipitation, change in porosity, thermal conductivity, and fractures of host rocks could affect mineralization as well. Our modeling of boiling phenomenon demonstrates similar characteristics with natural low-sulfidation epithermal gold‑silver deposits, suggesting that boiling is an important mechanism to cause vertical metal zoning in the low-sulfidation epithermal gold‑silver deposits, even though mixing could also be important to precipitate metals in some deposits.
Quantum sensitivity of squeezed Schrodinger cat states

Theoretically it is shown that high quantum sensitivity of the squeezed state of the Schrodinger cat relative to the non-squeezed state can be achieved. For realization of squeezing the Schrodinger cat state, a parametric process in a nonlinear optical crystal is used. To visualize the squeezed state of the Schrodinger cat with and without a displacement, the values of the Wigner functions are calculated.
Joint-ID: Transformer-Based Joint Image Enhancement and Depth Estimation for Underwater Environments

Underwater imaging is a challenging task due to factors such as scattering, absorption, and turbulence, which degrade image quality and limit visibility. In this article, we propose a novel approach for enhancing underwater images that leverages the benefits of joint learning for simultaneous image enhancement and depth estimation. We introduce <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Joint-ID</monospace> , a transformer-based neural network that can obtain high-perceptual image quality and depth information from raw underwater images. Our approach formulates a multimodal objective function that addresses invalid depth, lack of sharpness, and image degradation based on color and local texture. We design an end-to-end training pipeline that enables joint restoration and depth estimation in a shared hierarchical feature space. In addition, we propose a synthetic dataset with various distortions and scene depths for multitask learning. We evaluate <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Joint-ID</monospace> on synthetic and standard datasets, as well as real underwater images with diverse spectra and harsh turbidity, demonstrating its effectiveness for underwater image enhancement (UIE) and depth estimation. Furthermore, we demonstrate the ability of Joint-ID to perform feature matching and saliency detection for visually guided underwater robots. Our proposed method has the potential to improve the visual perception of underwater environments and benefit applications such as oceanography and underwater robotics. Supplements are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/joint-id/home</uri> .
Transformation of Bell states using linear optics

Bell states form a complete set of four maximally polarization entangled two-qubit quantum state. Being a key ingredient of many quantum applications such as entanglement based quantum key distribution protocols, superdense coding, quantum teleportation, entanglement swapping etc, Bell states have to be prepared and measured. Spontaneous parametric down-conversion (SPDC) is the easiest way of preparing Bell states and a desired Bell state can be prepared from any entangled photon pair through single-qubit logic gates. In this paper, we present the generation of complete set of Bell states, only by applying unitary transformations of half-wave plate (HWP) on the initial Bell state. The initial Bell state is prepared using a combination of a nonlinear crystal and a beam-splitter (BS) and the rest of the Bell states are created by applying single-qubit logic gates on the entangled photon pairs using HWPs. Our results can be useful in many quantum applications, especially in superdense coding where control over basis of maximally entangled state is required.
An unsupervised end-to-end approach to fault detection in delta 3D printers using deep support vector data description

Fault detection in 3D printers is crucial for safety and quality assurance, emphasizing proactive prediction over reactive rectification based on manufacturing factors. Presently, most detection techniques rely on shallow models with limited representational capabilities, necessitating manual feature extraction from the captured signals. This manual process is not only cumbersome and potentially costly but often requires intricate domain-specific knowledge. Additionally, these handcrafted features might not optimally distinguish between normal and faulty samples, potentially reducing prediction accuracy. In this study, we introduce an end-to-end approach using the Deep Support Vector Data Description model for fault detection in 3D printers. This design inherently facilitates automatic feature learning, where the features are synergistically optimized for fault detection. Our experiments leverage magnetic field signals for fault detection in 3D printers, using 1D convolutional layers to discern temporal signal patterns and wide kernels in the initial layer to mitigate high-frequency noise. Furthermore, our model can be easily adapted to integrate multi-channel signals for enhanced accuracy. Evaluations on real-world data from a delta 3D printer underscore the superiority of our method compared to existing alternatives.
Real-world applications of BIM and immersive VR in construction

The integration of immersive Virtual Reality (VR) and Building Information Modeling (BIM) has many applications within the Architecture, Engineering, and Construction (AEC) industries. In this context VR is often highlighted for its ability to convey scale and details, especially when compared to non-immersive visualizations. However, despite being an active area of research, there is currently a lack of real-world studies exploring immersive VR in a construction-oriented context. In addition, there are still technical challenges and barriers for an efficient integration, such as rendering performance and interoperability issues. This paper addresses these issues by investigating the use of immersive, single- and multi-user VR within the openBIM ecosystem. The contribution is two-fold: (a) an in-depth presentation of algorithms and technical details of a multi-user VR application for immersive visualization of large and complex BIMs and (b) an evaluation of this VR system on several real-world construction projects. In all cases the VR visualization has been directly realized from the design teams IFC-models and the multi-user sessions has been performed both co-located as well as fully remote. The results show that multi-user VR improves communication, understanding, and collaboration, and by letting staff with knowledge and experience from construction production review the project in VR, design errors and constructability issues can be identified and resolved before reaching the actual production stage. Moreover, the use of VR is helpful regarding sequencing and planning, and to identify alternative design solutions.
Harnessing federated generative learning for green and sustainable Internet of Things

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future.
Discriminative object tracking by domain contrast

Multi-domain tracking method as a novel learning paradigm improves object tracking by sharing domain information with a common backbone whilst learning private information with domain-specific layers. In that context, each individual video sequence as a specific domain serves for a domain-specific layer. In this paper, we observe an intriguing finding that target features from different domains are highly confused with each other, thus having weak discriminative ability, for lack of domain interaction. To this end, we propose a simple yet effective domain interaction training paradigm called domain contrast to boost discriminative object features by effectively using amounts of instances from all the domains in two novel aspects: (1) a light-weight memory-saving training algorithm is proposed to solve the “out-of-the-memory” problem, which paves the way to couple with previous multi-domain trackers, and (2) a composite class-balanced loss is explored to tackle a more practical imbalanced problem, which not only involves the usual class imbalance problem but also accounts for the case of the totally mere negative instances. Experiments on multiple popular tracking benchmark datasets show that our mechanism consistently achieves the tracking performance gain of both base multi-domain tracker and its real-time variant thereof, without any other changes made on the original network.
R2: Optimal vector-based and any-angle 2D path planning with non-convex obstacles

A novel vector-based path planner, R2 (R in two dimensions), is introduced in this paper. R2 is optimal and online, returning any-angle paths by applying heuristic costs to vector-based searches. R2 delays line-of-sight checks to expand the most promising path that has the least detours from the start and goal points. As delayed checks can cause severe path cost underestimates, R2 infers the smallest known convex hull, the best hull, of obstacles while moving around them. To construct the best hull, phantom points are introduced, which are imaginary turning points lying on non-convex corners to guide future searches. Tracing rules are introduced to ensure that the estimated path cost from the best hull increases admissibly and monotonically between queues to the open-list. The distance between the start and goal points have little impact on R2’s performance when compared to the number of line-of-sight checks that collide with obstacles. While having an exponential search time in the worst case with respect to the number of collided line-of-sight checks, R2 is much faster than state-of-the-art when the optimal path is expected to turn around few obstacles, especially on large maps with few disjoint and non-convex obstacles.
Constrained continuous-action reinforcement learning for supply chain inventory management

Reinforcement learning (RL) is a promising solution for difficult decision-making problems, such as inventory management in chemical supply chains. However, enabling RL to explicitly consider known environment constraints is crucial for safe deployment in practical applications. This work incorporates recent tools for optimization over trained neural networks to introduce two algorithms for safe training and deployment of RL, with a focus on supply chains. Specifically, we use optimization over trained neural-network state–action value functions (i.e., a critic function) to directly incorporate constraints when computing actions in a continuous action space. Furthermore, we introduce a second algorithm that guarantees constraint satisfaction during deployment by directly implementing actions from constrained optimization of a trained value function. The algorithms are compared against state-of-the-art algorithms TRPO, CPO, and RCPO using a computational supply chain case study.
Improved zeroing neural models based on two novel activation functions with exponential behavior

A family of zeroing neural networks based on new nonlinear activation functions is proposed for solving various time-varying linear matrix equations (TVLME). The proposed neural network dynamical systems, symbolized as Li-VPZNN1 and Li-VPZNN2, include an exponential parameter in nonlinear activation function (AF) that leads to faster convergence to the theoretical result compared to previous categories of nonlinearly activated neural networks. Theoretical analysis as well as numerical tests in MATLAB's environment confirm the efficiency and accelerated convergence property of the novel dynamics.