Reviewerly/exHarmony huggingface dataset:
Adaptive Ratio-Based-Threshold Gradient Sparsification Scheme for Federated Learning

Federated learning (FL) is a distributed learning paradigm that has received great attention over the past several years due to its privacy-preserving property. As the models involved in FL are usually dense and overparameterized however, various studies are being conducted in gradient sparsification to reduce the high communication overhead. While many of the recently-presented schemes that are variations of top-k have shown competitive inference accuracy convergence to the baseline “vanilla” FL, they have a fixed sparsity rate throughout all of the communication rounds, which leads to an unnecessary excessive transmission of gradients as the global model converges. Furthermore, the constant-threshold gradient sparsification method called Threshold-z-, that is well-known for its dynamic rate, does not account for the ratio between the gradient and the pre-update parameter value, causing some gradients that are orders of magnitude larger than the pre-update parameter values to be neglected in the following aggregation process. In this paper, we introduce a new algorithm that addresses both of these issues, called adaptive ratio-based-threshold gradient sparsification method. Our main idea is introducing a custom gradient sparsity threshold for each local parameter based on their pre-update value and a hyperparameter denoted as Ψ. We demonstrate through image classification experiments on MNIST and CIFAR-10 datasets in both independent-and-identically-distributed (IID) and non-IID settings that under optimal Ψ, the gradient sparsity rates adapt & increase as the global model converges, while simultaneously producing inference accuracies that are competitive to vanilla FL.
A Representation Learning Approach Incorporating Entity Descriptions and Types

Knowledge graphs have a wide range of applications in areas such as intelligent question and answer, personalized recommendation and intelligent decision making. Knowledge graph representation learning aims to address the sparsity and incompleteness of entities and relationships in knowledge graphs. Traditional representation learning methods based on translation operations such as TransE and TransH usually only consider the triadic information of the knowledge graph to learn the representation in isolation, ignoring the rich textual information, type information, etc., resulting in not mining the semantics carried by the entities in the knowledge graph itself. To address the shortcomings of the existing methods, this paper proposes a representation learning method that fuses entity description and type, introducing entity description and type information on the basis of the SRotatE model, aiming to learn triadic knowledge while fusing triadic factual information with entity description and type information, which can better express the semantics between entities and relationships, thus improving the performance of the knowledge graph representation learning model performance of the knowledge graph representation learning model. Experimental results on real datasets show that the proposed method outperforms mainstream models such as TransE, ComplEx and RotatE.
Leveraging Unsupervised Machine Learning for New Traffic Types Classification in Hybrid Satellite Terrestrial Network

By offering real-time insights, automating network modifications, and improving network performance, unsupervised machine learning can be a useful tool for managing novel traffic types in hybrid terrestrial-satellite networks. It aids network operators in adjusting to shifting traffic patterns and addressing the needs of a dynamic and ever-changing communication landscape. We investigate the possibility of unsupervised learning based on four different learning algorithms created for identifying novel traffic with the aim of establishing a novel framework that maximizes proactive resource allocation. Based on a performance assessment that was done, the findings show that that the K-Means Clustering and Gaussian Mixture Model provide the best report between acceptable silhouette and Principal Component Analysis.
Generalized Probabilistic Clustering Projection Models for Discrete Data

Projection and Clustering are two main approaches in text mining. The goal of projection is to map the high-dimensional data into a lower-dimensional latent space, where the clustering task is to categorize data into different groups based on their similarity features. Several methods have been proposed to retrieve relevant information based on the co-occurrence of the data. However, the majority of works do not examine the joint effect of the projection and clustering, especially the effect of the prior distribution in the case of discrete data. For this purpose, in this paper, we propose a novel approach using a probabilistic clustering-projection framework where Dirichlet distribution, generalized Dirichlet distribution, and Beta-Liouville distribution are implemented as priors to study the impacts of prior knowledge in the perplexity of the model. Using a variational EM algorithm we estimate latent variables associated with clustering and projection parameters, iteratively updating the lower bound of the log-likelihood until convergence. Our experimental results demonstrate reduced perplexity using generalized Dirichlet and Beta-Liouville priors compared to Dirichlet. Moreover, we evaluate the model's performance in word projection and document clustering tasks, finding that both generalized Dirichlet and Beta-Liouville outperform Dirichlet in these domains.
Building Nonlinear Regression Models for Estimating the Number of Clusters and Their Initial Centroids

The fundamental concept of partition-based clustering algorithms is to consider the data point’s center as the center of its associated cluster. In the initiation phase of clustering algorithms, notably the widely-researched k-means methodology, it is imperative to ascertain the number of clusters and establish their preliminary centroids (means). Now well-known methods define something one only, or the number of clusters, or initial centroids. We propose a technique for the simultaneous estimation of both the number of clusters and their initial centroids, which is based on building nonlinear regression models taking into account outliers. The use of nonlinear regressions is due primarily to the smaller width of their prediction intervals compared to the corresponding widths of linear regressions. Two application examples of the proposed technique using the four-variate Box-Cox normalizing transformation yield good results for the k-means clustering.
Mobilint’s ARIES: Chip for Edge AI

The advancement of AI technology has led to its widespread adoption in a variety of fields. However, in the edge environment, there is currently no AI chip that effectively meets the market’s demands for performance, power efficiency, and price. As a result, AI applications have not been fully realized in edge contexts. To address this gap, we present ARIES, an innovative AI chip developed by Mobilint, a fabless company headquartered in South Korea. ARIES embodies superior performance, high power efficiency, and competitive pricing. Mobilint offers ARIES in two distinct forms: the PCIe card MLA100 and the AI box MLX-A1, each designed to maximize utility. To further enhance user engagement, a user-friendly software development kit (SDK) named qb is also provided to simplify deployment.
MC-YOLOv5: A small target detection model based on YOLOv5

Small object detection is one of the most challenging tasks in object detection. The small size of objects, low resolution, and occlusion introduce difficulties in accurately detecting them. To address these challenges, we propose an improved small object detection model called MC-YOLOv5, based on YOLOv5. In MC-YOLOv5, we replace the backbone network with MobileNetV3 to enable the model to perform efficiently on mobile devices and better adapt to small object detection tasks. Furthermore, we enhance the feature extraction capability of the model by incorporating the CBAM (Convolutional Block Attention Mod-ule) attention mechanism into the detection head. In addition, we improve the data augmentation techniques by extending the traditional Mosaic data augmentation from combining 4 images to 9 images, thereby reducing the object size. We also introduce a synthetic fog algorithm to add masks to the images, which reduces the resolution and simulates noise. Experimental results on the COC02017 dataset demonstrate that our MC-YOLOv5 model performs better in small object detection. Compared to the traditional YOLOv5, our model shows an approximate 7% improvement in accuracy and significant enhancements in metrics such as mean average precision (mAP).
Methods of Organization and Navigation of Big Data in Multimedia Content

The practical application of this development lies in improving the user experience by offering an effective and intuitive solution for organizing and navigating large volumes of multimedia data. The focus of the research is the development of an efficient methodology for automatically detecting sections from multimedia content, based on transcript and timing analysis.
Improved Architecture and the Synthesis Algorithm for Bithreshold Neural Network Classifier

The model of the 3-layer feed-forward neural network is introduced whose first hidden layer consists of bithreshold neurons and the other layers—of single-threshold ones. The proposed network is capable to recognize compact finite set of patterns using a union of hyperrectangular decision regions in n-dimensional space. We design a multiclass classifier on the base of such network, propose the synthesis algorithm for it and estimate the networks size as well as the time of computations. The simulation results demonstrate that the application of the additional hidden layer improves the accuracy of classification.
Using Twitter to collect a multi-dialectal corpus of Albanian using advanced geotagging and dialect modeling

In this study, we present the acquisition and categorization of a geographically-informed, multi-dialectal Albanian National Corpus, derived from Twitter data. The primary dialects from three distinct regions-Albania, Kosovo, and North Macedonia-are considered. The assembled publicly available dataset encompasses anonymized user information, user-generated tweets, auxiliary tweet-related data, and annotations corresponding to dialect categories. Utilizing a highly automated scraping approach, we initially identified over 1,000 Twitter users with discernible locations who actively employ at least one of the targeted Albanian dialects. Subsequent data extraction phases yielded an augmentation of the preliminary dataset with an additional 1,500 Twitterers. The study also explores the application of advanced geotagging techniques to expedite corpus generation. Alongside experimentation with diverse classification methodologies, comprehensive feature engineering and feature selection investigations were conducted. A subjective assessment is conducted using human annotators, which demonstrates that humans achieve significantly lower accuracy rates in comparison to machine learning (ML) models. Our findings indicate that machine learning algorithms are proficient in accurately differentiating various Albanian dialects, even when analyzing individual tweets. A meticulous evaluation of the most salient attributes of top-performing algorithms provides insights into the decision-making mechanisms utilized by these models. Remarkably, our investigation revealed numerous dialectal patterns that, despite being familiar to human annotators, have not been widely acknowledged within the broader scientific community.
3D dense micro-block difference descriptor for gastrointestinal disease detection

At present, medical endoscopy is the main procedure for exploring the internal cavities of the human body. Developing methods for integrating image processing and endoscopic visualization improves image quality and accurately identifies cancerous abnormalities. This paper aims to improve the efficiency detection of gastrointestinal disease detection in the endoscopic video. A recognition method is proposed, which is based on a 3-D binary difference of micro blocks and consists of the following steps: 1) a sequence of frames is divided into 3-D cuboids; 2) inside each cuboid, patches of different sizes are built, which are used to obtain volumetric local binary templates; 3) the Hamming distance is calculated between a randomly selected pair of cuboids within each patch of the video clip, which is sequentially written to a separate vector descriptor; 4) for recognition, the method of machine learning is used. Experimental studies show that disease detection is performed accurately on the test dataset (Kvasir).
A Network Representation Learning Model Based on Multiple Remodeling of Node Attributes

Current network representation learning models mainly use matrix factorization-based and neural network-based approaches, and most models still focus only on local neighbor features of nodes. Knowledge representation learning aims to learn low-dimensional dense representations of entities and relations from structured knowledge graphs, and most models use the triplets to capture semantic, logical, and topological features between entities and relations. In order to extend the generalization capability of the network representation learning models, this paper proposes a network representation learning algorithm based on multiple remodeling of node attributes named MRNR. The model constructs the knowledge triplets through the textual association relationships between nodes. Meanwhile, a novel co-occurrence word training method has been proposed. Multiple remodeling of node attributes can significantly improve the effectiveness of network representation learning. At the same time, MRNR introduces the attention mechanism to achieve the weight information for key co-occurrence words and triplets, which further models the semantic and topological features between entities and relations, and it makes the network embedding more accurate and has better generalization ability.
Single-shot dispersive interferometry for inline surface inspection

The dispersive interferometry provides an instantaneous surface measurement in a single camera frame, making it resistant to environmental disturbances and ideal for in-process surface metrology. It also benefits from the extended measurement ranges in both depth and lateral directions by incorporating hyperspectral imaging technology and cylindrical beam illumination, respectively. This paper reports on an in-house developed cylindrical lens-based dispersive interferometer for high-accuracy surface inspection, particularly for structured surfaces. The obtained spectral interferogram is analyzed using the fringe order algorithm, in which the phase slope method is used to calculate the initial height to resolve the fringe order ambiguity and eventually an improved height value can be obtained using the exacted phase of a single wavelength. Experiments demonstrate that the measurement noise of the developed interferometry system is less than 1 nm within the measurement range. A brass step sample made by a diamond turning machine was measured and the experimental results closely align with those given by the commercial white light interferometer -Talysurf CCI 3000.
Center-Guided Transformer for Panoptic Segmentation

A panoptic segmentation network to predict masks and classes for things and stuff in images is proposed in this work. Recently, panoptic segmentation has been advanced through the combination of the query-based learning and end-to-end learning approaches. Current research focuses on learning queries without distinguishing between thing and stuff classes. We present decoupling query learning to generate effective thing and stuff queries for panoptic segmentation. For this purpose, we adopt different workflows for thing and stuff queries. We design center-guided query selection for thing queries, which focuses on the center regions of individual instances in images, while we set stuff queries as randomly initialized embeddings. Also, we apply a decoupling mask to the self-attention of query features to prevent interactions between things and stuff. In the query selection process, we generate a center heatmap that guides thing query selection. Experimental results demonstrate that the proposed panoptic segmentation network outperforms the state of the art on two panoptic segmentation datasets.
Measurement and analysis of a 4″-18″ aperture dual-beam Fizeau interferometer

A dual-beam Fizeau interferometry with both small and large aperture two measurement modes is proposed. The two modes of the interferometer were measured and analyzed using three-flat four-step absolute measurement and three-flat simulated sinusoidal phase grating. An integrated 4″-18″ aperture dual-beam Fizeau interferometer was used to perform experiments on large and small aperture by the above two measurement methods. The experimental results show that the absolute surface errors of the three flats are less than λ/20(PV) and λ/100(RMS), and the transfer function is better than 0.78 at the 1 mm-1 spatial frequency, which satisfies the specification.
optimization of adaptive digital fringe method in PMD

When using Phase Measurement Deflectometry (PMD) to measure a surface with multiple levels of reflectivity, complete phase information can be obtained by reducing exposure. This enables point-to-point mapping of the imaging surface and projection surface. Through these operations, adjustments can be made to the intensity of the light source, resulting in the division of the light source into regions. The ultimate goal is to achieve high-precision phase measurement of objects with multiple levels of reflectivity. However, the adjustment of light intensity at the location of sudden changes in reflectivity can lead to mutual interference between adjacent<sup>1</sup> reflectivity regions. Due to the fact that the focusing surface of the camera is usually on the measuring surface, the position of the display is away from the focus. This results in a point on the imaging surface corresponding to diffuse plaques on the display. The intensity of the light source within the diffuse plaques can impact the imaging intensity. Therefore, imaging points located at the edge are highly susceptible to the influence of modulation intensity on both sides, resulting in darker low reflectivity areas at the edge, brighter high reflectivity areas at the edge, and even overexposure. In order to solve this problem, this paper analyzed the defocusing situation of the system and eliminates this influence through optical methods.
Design and Exploration of Real Time Monitoring System for Power Grid Operation Safety Based on YOLO Object Detection Algorithm

As a fundamental industry of the national economy, the development level of the power industry directly affects the national economic construction and people's lives. With the continuous acceleration of modernization, higher requirements have been put forward for the safe operation of the PG. Therefore, it is necessary to achieve the integration of remote real-time monitoring, management, and maintenance under the current situation, and combine modern information technology methods for research and analysis. To meet the system requirements and ensure the quality of power supply, a suitable and efficient job safety management system platform is designed for the practical situation of power grid (PG) operation safety, and is based on the YOLO (You Only Look Once) algorithm operating environment. This platform can be effectively applied in daily electricity management work to improve the production efficiency and economic benefits of PG enterprises. Afterwards, this article also conducted functional testing on the security real-time monitoring system, and the test results showed that the accuracy of the system reached over 80%; the recall rate was over 85%; the average accuracy was between 0.4 and 0.7; the system stability was between 90% and 97%; the scalability of the system was over 93%.
Automatic Design of Microcontroller System Simulation Based on Artificial Intelligence Technology and Data Intelligence Analysis

The automatic design of system simulation based on microcontroller can effectively improve the intelligence level of microcontroller system simulation through intelligent analysis of data and combined with AI (Artificial Intelligence) technology. On the basis of analyzing AI technology, the article explored the process of automatic design for microcontroller system simulation, and proposes a microcontroller system simulation automatic design method based on AI technology and data intelligence analysis. Finally, through experiments, it was found that the quality qualification rate of the microcontroller system increased from 76.74% to 85.48%, verifying the effectiveness of this method.
Building Engineering Cost Prediction Model Based on TSNE and Improved Grey Correlation Algorithm

With the development of digitalization in the construction industry, the complexity of construction projects is increasing. In response to the widely valued cost and quality management of construction projects, improved algorithms based on machine vision learning can effectively cope with the increasingly complex cost prediction of construction projects. In the entire model design, how to complete the selection and processing of key indicators for construction projects is currently a key issue that needs to be urgently solved. This article studied the use of TSNE (T-Distributed Stochastic Neighbor Embedding) and improved grey correlation algorithm to design a cost prediction model, aiming to improve the prediction accuracy of construction project costs and reduce system prediction time. This article analyzed the cost index system and simulation experiments of construction projects, applied machine learning based TSNE and improved grey correlation algorithm, combined index dimensionality reduction processing and construction project unit test set samples, and discussed the following conclusions based on the data results: In the context of intelligent large-scale infrastructure, the construction project cost prediction model using TSNE and improved grey correlation algorithm had higher prediction accuracy than traditional prediction methods in all construction project samples, with an average improvement of 5.1%. At the same time, in terms of model prediction efficiency, the improved algorithm was better than traditional prediction methods, with an average improvement of 12.75%. This indicated that the construction project cost prediction model based on TSNE and improved grey correlation algorithm has a relatively good prediction effect overall.
Research on multi-objective optimization of construction engineering based on improved genetic algorithm

The role of cost, time and labor intensity in the construction process is very important, but there is a problem of poor optimization effect. The standard genetic algorithm cannot solve the problem of index optimization in the construction process, and the optimization rationality is low. Therefore, this paper proposes an improved genetic algorithm to study the objectives of construction projects. Firstly, the k-value clustering theory is used to analyze the characteristic values of the index and divide them according to the changing trend to reduce the disturbance factors in optimization. Then, the k-value clustering theory is used to search for the child-parent node of the indicator to form the final optimization result and complete the multi-objective optimization. MATLAB simulation shows that under certain optimization criteria, improving the genetic algorithm can improve the rationality of multi-objective optimization, and the optimization time is shorter than that standard improves genetic algorithms.