Reviewerly/exHarmony huggingface dataset:
White Blood Cell Classification Using Pre-Trained Deep Neural Networks and Transfer Learning

The accurate classification of white blood cells is crucial for diagnosing and monitoring various medical conditions. Traditional methods of diagnosis are expensive, time-consuming, and depend on specialists' expertise. Image processing-based techniques provide a faster and simpler approach to detecting abnormalities in white blood cells. In this study, a computer-aided diagnosis system was proposed that utilizes pre-trained networks for accurate classification of white blood cells. The dataset used in this study is sourced from Kaggle, and the classification process is performed without image segregation or feature extraction techniques. Pretrained series networks are employed for classification, and a classification accuracy of 99% is achieved using the Inception-ResNet-v2 network with the Adam optimizer. The comparative analysis aids in understanding the performance and suitability of different network architectures for white blood cell classification tasks. The results showcase the potential of the proposed computer-aided diagnosis system for accurate and efficient white blood cell classification, contributing to advancing the automation and reliability of white blood cell analysis and supporting improved disease diagnosis and monitoring.
Weighted guided image filtering with entropy evaluation weighting

Although the guided image filter (GIF) is an excellent edge-preserving filter, it generally suffers from halo artifacts due to the local property and the fixed regularization parameter. To address the problem, a weighted guided image filter (WGIF) was proposed by incorporating an edge-aware weighting into the GIF. In the filtering process, WGIF employs an averaging strategy for edge-aware weighting. Although the averaging strategy is a highly efficient method, it is susceptible to extreme values and tends to obscure critical factors, so it often leads to inaccurate results. Consequently, the output results quality of the WGIF is often degraded. To remedy the deficiency, a weighted guided image filter with entropy evaluation weighting (EEW-WGIF) is proposed in this paper. EEW-WGIF employs an edge-aware weighting strategy based on entropy evaluation method to detect edges more accurately, and incorporates an explicit constraint based on the gradient variation to better preserve edges. To verify the filtering effectiveness of the EEW-WGIF, it was applied to edge-preserving smoothing filtering, exposure images fusion, single image detail enhancement, structure-transferring filtering and image denoising. Experimental results show that the proposed filter can achieve excellent performance in both visual quality and objective evaluation.
Dynamics and Concealment of Time-Delay Signature in Mutually Coupled Nano-Laser Chaotic Systems

It is well known that nano-lasers (NLs), as important optical components, have attracted widespread attention for their output characteristics. In this paper, the dynamic behavior and time-delay concealment properties of NLs mutually coupled in open-loop, semi-open-loop, and closed-loop structures have been numerically investigated. We employ bifurcation diagrams and 0–1 chaos tests in our simulations to quantitatively analyze the dynamic properties of the system and introduce the autocorrelation function to evaluate the ability of the system to conceal the time-delay signature (TDS). In the meantime, the effects of the NL parameters and the controllable variables of the system on the TDS are studied. The results indicate that, compared with an open-loop structure without feedback, the mutual coupling scheme with added feedback is beneficial for the system to output high-quality chaotic signals. Furthermore, selecting a moderate Purcell factor F and a smaller spontaneous emission coupling factor β can achieve TDS concealment over a wider parameter range of injection intensity and frequency detuning.
Class-level Structural Relation Modeling and Smoothing for Visual Representation Learning

Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models mainly rely on the classification loss to implicitly regularize the class-level data distributions, and they may face difficulties when handling classes with diverse visual patterns. We argue that the incorporation of the structural information between data samples may improve this situation. To achieve this goal, this paper presents a framework termed Class-level Structural Relation Modeling and Smoothing for Visual Representation Learning (CSRMS), which includes the Class-level Relation Modelling, Class-aware Graph Sampling, and Relational Graph-Guided Representation Learning modules to model a relational graph of the entire dataset and perform class-aware smoothing and regularization operations to alleviate the issue of intra-class visual diversity and inter-class similarity. Specifically, the Class-level Relation Modelling module uses a clustering algorithm to learn the data distributions in the feature space and identify three types of class-level sample relations for the training set; Class-aware Graph Sampling module extends typical training batch construction process with three strategies to sample dataset-level sub-graphs; and Relational Graph-Guided Representation Learning module employs a graph convolution network with knowledge-guided smoothing operations to ease the projection from different visual patterns to the same class. Experiments demonstrate the effectiveness of structured knowledge modelling for enhanced representation learning and show that CSRMS can be incorporated with any state-of-the-art visual representation learning models for performance gains. The source codes and demos have been released at https://github.com/czt117/CSRMS.
The use of social networks in the professional development of pedagogical workers

The article shows that the pedagogical workers of the new generation need to create innovative conditions that meet the demands of the time for professional development and personal development. It was found that social networks provide the teacher with a number of new opportunities for improving professional skills and self-realization. The practical issues of using social networks in the professional development of pedagogical workers are revealed. The concept of “social network” in the perspective of the professional development of pedagogical workers has been clarified, namely: a social network is a tool for building an open informational and discussion space, within which specialists of various fields can exchange experience; accumulate ideas and identify problematic issues of science and education, find common ways to solve them; find or generate points of contact of their professional interests. The directions of using social networks in the professional development of pedagogical workers are highlighted: participation in online professional associations, communities of interest, groups; maintenance of online professional blogs, thematic pages, professional portfolio sites; creation and use of digital educational content. The practical issues of the use of social networks in the professional development of pedagogical workers in each of the highlighted areas are characterized, which will contribute to the formation of a complex of theoretical knowledge and practical skills for the productive use of social networks among specialists in various fields. Prospective areas of further scientific research are the preparation of pedagogical workers for the use of educational social networks in pedagogical practice.
Web3 Metaverse: State-of-the-Art and Vision

The metaverse, as a rapidly evolving socio-technical phenomenon, exhibits significant potential across diverse domains by leveraging Web3 (a.k.a. Web 3.0) technologies such as blockchain, smart contracts, and non-fungible tokens (NFTs). This survey aims to provide a comprehensive overview of the Web3 metaverse from a human-centered perspective. We (i) systematically review the development of the metaverse over the past 30 years, highlighting the balanced contributions from its core components: Web3, immersive convergence, and crowd intelligence communities, (ii) define the metaverse that integrates the Web3 community as the Web3 metaverse and propose an analysis framework from the community, society, and human layers to describe the features, missions, and relationships for each community and their overlapping sections, (iii) survey the state-of-the-art of the Web3 metaverse from a human-centered perspective, namely, the identity, field, and behavior aspects, and (iv) provide supplementary technical reviews. To the best of our knowledge, this work represents the first systematic, interdisciplinary survey on the Web3 metaverse. Specifically, we commence by discussing the potential for establishing decentralized identities (DID) utilizing mechanisms such as profile picture (PFP) NFTs, domain name NFTs, and soulbound tokens (SBTs). Subsequently, we examine land, utility, and equipment NFTs within the Web3 metaverse, highlighting interoperable and full on-chain solutions for existing centralization challenges. Lastly, we spotlight current research and practices about individual, intra-group, and inter-group behaviors within the Web3 metaverse, such as Creative Commons Zero license (CC0) NFTs, decentralized education, decentralized science (DeSci), and decentralized autonomous organizations (DAO). Furthermore, we share our insights into several promising directions, encompassing three key socio-technical facets of Web3 metaverse development.
Relevance gradient descent for parameter optimization of image enhancement

Machine learning plays a pivotal role in constructing learning models and leveraging abundant image data for feature extraction, which is essential for achieving accurate and efficient image enhancement through optimization algorithms. Traditional gradient descent methods are confined to optimizing independent parameters, while recent algorithms neglect the interdependence among parameters, resulting in prolonged iteration times for each method. To address this issue, we propose a novel Correlation-Based Gradient Descent (RGD) algorithm tailored for enhancing low-light images. RGD leverages inter-iteration results for mutual parameter transmission and optimization, employing convergence or oscillation criteria to select the final enhanced image and find the optimal parameter combination for image enhancement, thereby enhancing parameter optimization efficiency. Specifically, each iteration is divided into multiple operations, with each operation adopting different enhancement schemes. During each iteration, the results of different operation parameters are shared, which facilitates the discovery of optimal parameters for low-light image enhancement. Subsequently, corresponding enhancement operations are performed based on this parameter combination to optimize standard indicators. Our experiments on medical and natural low-light scenarios demonstrate that our RGD method achieves a significant reduction in iteration time, with at least a 17% decrease compared to exhaustive loop methods.
An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search

Searching for similar objects in intrinsically high-dimensional data sets is a challenging task. Sketches have been proposed for faster similarity search using linear scans. Binary sketches are one such approach to find a good mapping from the original data space to bit strings of a fixed length. These bit strings can be compared efficiently using only few XOR and bit count operations, replacing costly similarity computations with an inexpensive approximation. We propose a new scheme to initialize and improve binary sketches for similarity search on the unit sphere, i.e., for cosine similarity. Our optimization iteratively improves the quality of the sketches with a form of orthogonalization. We provide empirical evidence that the quality of the sketches has a peak beyond which it is not correlated to neither bit independence nor bit balance, which contradicts a previous hypothesis in the literature. Regularization in the form of noise added to the training data can turn the peak into a plateau and applying the optimization in a stochastic fashion, i.e., training on smaller subsets of the data, allows for rapid initialization.
Computational Enhancements of HNSW Targeted to Very Large Datasets

The Hierarchical Navigable Small World (HNSW) Graph is a graph-based approximate similarity search algorithm that achieves fast and accurate search through a hierarchical structure providing long-range and short-range links. The HNSW remains as a state-of-the-art method, as shown by this submission to the SISAP 2023 Indexing Challenge. This submission introduces a modification to the implementation of HNSW that avoids the cost of a batched construction and drastically reduces disk-space of the saved index when working with large datasets. Through the lens of this competition, this work provides a careful analysis of several important factors for high-performance applications, including the removal of unnecessary functionality, the use of SIMD vectorization for distance computations, and optimal utilization of cache through spatial locality and cache prefetching.
Teleportation of quantum coherence

We investigate whether it is possible to teleport the coherence of an unknown quantum state from Alice to Bob by communicating a smaller number of classical bits in comparison to what is required for teleporting an unknown quantum state. We find that we cannot achieve perfect teleportation of coherence with one bit of classical communication for an arbitrary qubit. However, we find that if the qubit is partially known, i.e., chosen from the equatorial and polar circles of the Bloch sphere, then teleportation of coherence is possible with the transfer of one cbit of information when we have maximally entangled states as a shared resource. In the case of the resource being a nonmaximally entangled state, we can teleport the coherence with a certain probability of success. In a general teleportation protocol for coherence, we derive a compact formula for the final state at Bob's laboratory in terms of the composition of the completely positive maps corresponding to the shared resource state and a joint positive operator-valued measure (POVM) performed by Alice on her qubit and the unknown state. Using this formula, we show that teleportation of the coherence of a partially known state with real matrix elements is perfectly possible with the help of a maximally entangled state as a resource. Furthermore, we explore the teleportation of coherence with Werner states and show that even when Werner states become separable, the amount of teleported coherence is nonzero, implying the possibility of teleportation of coherence without entanglement.
Improve coati optimization algorithm for solving constrained engineering optimization problems

Abstract The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (i) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half wait beneath to catch it and (ii) Coatis avoidance predators behavior, which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm’s performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coati optimization algorithm (ICOA) to enhance the algorithm’s efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm’s exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm’s global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems.
Ingestion of Google Cloud Platform Data using Dataflow

Organizations using big data analytics to enhance operations often load data into a cloud-based data warehouse. Dataflow on Google Cloud Platform (GCP) lets users extract, manipulate, and load data from diverse sources into Google Cloud Storage or Big Query. This article discusses Dataflow's advantages for GCP data import and how to build up a pipeline. Data intake and data-driven decision-making start the article. It then presents Dataflow, which can process batch and stream data and grow automatically dependent on data volume. The report also emphasizes the advantages of adopting Dataflow over alternative data ingestion solutions, such as its smooth interaction with GCP services, its user-friendly UI, and its cost-effectiveness. The article walks through building a GCP project, setting up Google Cloud Storage or Big Query as the destination for ingested data, launching a Dataflow task, and monitoring its progress. The study also examines data storage type, pipeline runner, and pipeline parallelism recommended practices for Dataflow pipeline performance. The article concludes with Dataflow use examples for real-time analytics for e-commerce websites, data transfer from on-premises databases to the cloud, and log analysis for system behavior abnormalities. The article finishes by underlining the necessity of data intake in the era of big data and how Dataflow may help companies maximize data value by simplifying data ingestion and analysis. Dataflow for data input in GCP and how it may help enterprises employ big data analytics to expand and enhance operations are covered.
Deep Learning object detection models: evolution and evaluation

Computer vision is a subfield of artificial intelligence that relies on training computers to obtain a high level of understanding of vision data. A computer vision system aims at identifying objects through the acquisition of their features such as textures, shapes, sizes, colors, spatial arrangement, to gain an exhaustive description of a video or an image. There are a lot of subfields of computer vision one of them is Object Detection. Detecting objects is a task to identify objects in a specific area. Over the last decades, object detection has gained attention due to its wide range of applications such as human motion analysis, robot navigation, event detection, anomaly detection, video surveillance, traffic analysis, and security. In this paper, we are going to introduce the different Object Detection methodologies and especially relied on Deep Learning based on two categories one stage detectors and two stage detectors. The main goal of this study is to detect, analyze and compare several detection methods and identify the best method based on different several performance metrics ranging from 2014 to 2021. The purpose of this paper to compare some of Object detection methodologies using Weighted Scoring Model (WSM). This covers, studying those algorithms, selecting relevant algorithms. The result of this comparison will show the best Object Detection methods applied on COCO dataset.
Dense-and-Similar Object detection in aerial images

The general object detection performance has been improving significantly due to the prosperity of deep learning. When applied to aerial images, these algorithms perform poorly. There are, as we summarized, two practical reasons: (1) photographed from far above, objects are commonly small in size, dense in clusters, but sparsely scattered in the whole image; (2) some classes are naturally similar in appearance, not to mention the insufficiency of distinguishable visual features captured by cameras due to factors such as bad weather, various angles of view and long distances, etc. To address the two issues, we propose a dense-and-similar object detector with four key components: a coarse detector, an adaptive clustering procedure, a similar-class classifier, and a fine detector. At first, we input the original image into both the coarse and fine detectors. Then we cluster and patch the coarsely detected results adaptively to form a foreground region image. We feed the patched image into the coarse detector again and use the similar-class classifier to re-identify the labels of detected bounding boxes. At last, we put re-identified detections and outputs of the fine detector together, and obtain the final detections after non-maximal suppression. The approach proposed in this study achieved excellent results on both the VisDrone 2019 and DIOR datasets through experimental validation.
Improved artificial gorilla troops optimizer with chaotic adaptive parameters - application to the parameter estimation problem of mixed additive and multiplicative random error models

Abstract For mixed additive and multiplicative random error models (MAM models), due to the complex correlation between the parameters and the model power array, derivative operations will be inevitable in the actual calculation. When the observation equation is in nonlinear form, the operations will be more complicated. The swarm intelligence optimization algorithm (SIO) can effectively solve the derivative problem when estimating the nonlinear model parameters using conventional iterative algorithms. However, for different problems, the conventional SIO cannot effectively balance the ability of global and local behavior, resulting in the algorithm falling into prematureness and failing to output effective parameter information. To address the above problems, the improved artificial gorilla troops optimizer (CAGTO) algorithm with chaotic adaptive behavior is proposed. To address the problem that the population generated by the algorithm using pseudo-random numbers in the initialization population phase has poor traversability in the feasible domain, the chaotic sequence is applied to initialize the population instead of pseudo-random number generation to ensure that the population can traverse the feasible domain as much as possible and improve the global search capability of the algorithm. Adaptive parameters that vary linearly and nonlinearly with the algorithm process are constructed to balance the global search and local search ability, while accelerating the convergence speed. Two CAGTO algorithms with different parameter settings are constructed for different problems, and the experimental results show that both CAGTO algorithms can effectively solve the parameter estimation problem of MAM models with different nonlinear forms of observation equations compared with several other comparative algorithms.