CMLDS

Federated Learning (FL) is a learning architecture in which multiple clients use local data to train gradients and submit them to a server for global aggregation. However, achieving reliable federated learning in untrusted environments is challenging. ...

Network traffic prediction plays an important role in network management and network operation and maintenance. Traditional network traffic prediction models do not take into account the impact of network routing paths on network performance, nor the ...

We propose a new quantum-walk-based hash function QHF2M by combining two types of quantum walks with two-step memory and numerically test its statistical performance. The test result shows that QHF2M is on a par with the state-of-the-art hash functions ...

Present-day state of the art models can perform well on most language tasks. Math word problems are at the intersection of linguistic semantics and quantitative logic. Two salient state of the art methods to solve math word problems are evaluated ...

Analysis of competing cigarette products is crucial to improving product quality and brand popularity. Additionally, it is a vital part of tobacco industry research content. Due to traditional analysis methods relying on manual experience to identify ...

Target lost and robustness to occlusion scenarios hinder real-world applications of current object tracking methods. In this paper, we focus on reducing the frames of target lost in the single object tracking task. We propose a multi-template object ...

An analysis of the spatial and temporal variations and distribution of soil moisture (SM) in Lanzhou City was performed to reveal the degree of influence that meteorological elements and the non-stationary relationship between time and space have on ...

This research focuses on advancing RE methodologies by employing and comparing various NLP models for analyzing medical relationships, particularly concerning Gastroesophageal Reflux Disease (GERD). Leveraging a comprehensive dataset of GERD-related ...

Confronted with an abundance of adjustable parameters and ever-shifting workloads, database configuration tuning grapples with persistent challenges. The intricate task of thoroughly optimizing all these configuration "knobs" to attain peak performance ...

In this paper, we address the critical issue of predicting survival in individuals with Diabetes-related complications, considering the interconnected nature of these complications. Noncommunicable diseases (NCDs) such as Type-I and Type-II diabetes ...

Anomaly detection in power energy is crucial for energy efficiency. This aspect of electricity management assures for smooth functioning of vital sectors such as health, education and communications, particularly in developing countries. However, it is ...

Consensus filtering algorithms are commonly used for distributed state estimation. In addressing the challenge of unknown colored noise in distributed sensor networks, we propose a consensus filter based on Gaussian process regression (GPR). Our method ...

As the demand for leather increases, automated leather defect identification is becoming increasingly important in the leather industry. Based on the issues that unobvious color of leather defects, small defect area and low recognition efficiency, this ...

An adaptive multiple measurement model filter in the presence of outliers is proposed, which considers the uncertainty measurement noise including unknown model transition probability and noise covariance. in order to deal with the outliers, Gaussian ...

Optimizing the existing data readback system of heavy ion accelerator power supplies, a new data measurement system was designed on an independently developed digital power controller. This was aimed at replacing the existing power readback systems on ...

Graph-based multi-view clustering methods have demonstrated impressive outcomes in capturing the underlying manifold structure of data, leading to improved clustering performance. However, conventional graph-based methods overlook the significance of ...

The externalization of the state of one's mind, which people refer to as “mind reading” in science fiction, is currently being realized through brain decoding research. This field of study aims to deepen our understanding of the human brain, which is ...

Investors frequently turn to Twitter as a critical resource for gaining insights into cryptocurrency. The cryptocurrency market could inflate through influencing messages. This paper explores a few off-the-shelf Hugging Face's BERTs (Bidirectional ...

Short text can lead to sparse feature representation and classification inaccuracies due to noise and other issues. To address this, we propose a short text classification model that uses convolutional upsampling feature enhancement. Our approach ...

Federated Learning(FL) enables geographically dispersed organizations to collaboratively train a machine learning model. In this process, a parameter server enables global updating and synchronization of model by receiving and aggregating model data from ...

As academic research progresses, the blind review process of dissertations faces challenges in reviewer selection while ensuring independence, fairness, and objective evaluation. Existing systems and research often rely solely on text representation and ...

This study delves into the analysis of public sentiment within Azerbaijani social media comments and news articles employing Natural Language Processing methodologies and sentiment analysis techniques. The research explores distinct sentiment narratives ...

Online social networks contain a wealth of multimodal data with text and images, providing a promising new opportunity for the early detection of depression. Most existing depression detection methods rely on single modality or simply splice different ...

Traditional methods for evaluating the operating status of cigarette markets suffer from limitations such as coarse granularity, low efficiency, and inaccurate results. To address these challenges, this paper proposes an intelligent evaluation model ...

Abstract—The existing methods of solving the issue of low-voltage platform area is too simple and crude, robbing Peter to pay the wall. This paper solves the issue of low voltage by positioning weak points in a distribution network. This paper studies on ...

At present, Orthogonal Frequency Division Multiplexing (OFDM) technology has been widely used in underwater visible light systems. Partial Transmit Sequence (PTS), as one of the effective techniques to suppress the Peak-to-Average Power Ratio (PAPR) of ...

To improve the positioning accuracy and efficiency of indoor visible light communication, a new indoor visible light positioning method based on the Chaotic Seagull Optimization Algorithm (CSOA) is proposed in this paper. Firstly, this paper analyzes the ...

Distributed photovoltaic power stations are small in scale, which can make full use of idle areas such as roofs to generate electricity and improve power output. However, this also brings problems of operation and maintenance management. Each power ...

Abstract—At present, electromagnetic wave is mainly used for communication in mining area, but due to electromagnetic interference and other factors, the communication effect in the underground part of mining area is poor, and the installation and use ...

Abstract—to address this problem of the efficiency in a V-BLAST system, an Innovative Power-restricted Allocation Algorithm in the V-BLAST System with the Adaptive Modulation using the Low Complexity was proposed in this paper. The presented method can ...