Reviewerly/exHarmony huggingface dataset:
Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning

With the rapid development of the air transportation industry, the air traffic situation is becoming more and more complicated. Determining the situation of air traffic is of great significance to ensure the safety and smoothness of air traffic. The strong subjectivity of assessment criteria, inaccurate assessment results and weak systemic assessment method are the main problems in air traffic situation assessment research. The aim of our research is to present an objective and accurate situation assessment method for air traffic systems. The paper presents a model to assess air traffic situation based on the complex network theory and ensemble learning. The air traffic weighted network model was introduced to systematically describe the real state of an air traffic system. Assessment criteria based on the complex network analysis method can systematically reflect the operational state of an air traffic weighted network system. We transformed the air traffic situation assessment into a binary classification, which makes situation assessment objective and accurate. Ensemble learning was introduced to improve the classification accuracy, which further improves the accuracy of the situation assessment model. The model was trained and tested on the dataset of the East China air traffic weighted network in 2019. Its average classification accuracy is 0.98. The recall and precision rates both exceed 0.95. Experiments have confirmed that the situation assessment model can accurately output air traffic situation value and situation level. Furthermore, the assessment results are consistent with the real operational situation of the air traffic in East China.
Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers

Hand gesture recognition is an essential Human–Computer Interaction (HCI) mechanism for users to control smart devices. While traditional device-based methods support acceptable recognition performance, the recent advance in wireless sensing could enable device-free hand gesture recognition. However, two severe limitations are serious environmental interference and high-cost hardware, which hamper wide deployment. This paper proposes the novel system TaGesture, which employs an inaudible acoustic signal to realize device-free and training-free hand gesture recognition with a commercial speaker and microphone array. We address unique technical challenges, such as proposing a novel acoustic hand-tracking-smoothing algorithm with an Interaction Multiple Model (IMM) Kalman Filter to address the issue of localization angle ambiguity, and designing a classification algorithm to realize acoustic-based hand gesture recognition without training. Comprehensive experiments are conducted to evaluate TaGesture. Results show that it can achieve a total accuracy of 97.5% for acoustic-based hand gesture recognition, and support the furthest sensing range of up to 3 m.
An optimal system for increasing the contrast resolution qualities of histopathology images in the wavelet domain

Abstract In the field of histopathology, it has been observed that most images are of poor quality as a result of inadequate luminance and contrast. The first step in identifying and analyzing them is to enhance their quality while maintaining their inherent characteristics. This paper presents a novel method for enhancing histopathology images' important features, brightness preservation, and contrast by utilizing the adaptive intensity transformation gamma function in the wavelet domain. The RGB histopathological color image is first converted to the HSV color model. The V component is then stretched in order to compensate for any color distortion. In the proposed scheme, the stretched V component is split into four sub‐bands using discrete wavelet transform (DWT). Next, it determines the optimal value of the gamma correction function in the DWT lower sub‐band. A correction factor is applied using the singular value decomposition (SVD) technique to determine the optimal gamma function value using the nature‐inspired particle swarm optimization method (NIPSO). The corrected LL sub‐band is then applied to the inverse DWT (IDWT) together with the other unprocessed sub‐bands. In addition, the contrast of each feature is enhanced by employing an adaptive histogram equalization model that is contrast‐limited. The proposed method was evaluated through experiments and validations on a variety of histopathology images retrieved from multiple databases. Several quantitative studies have demonstrated that the proposed method outperforms existing enhancement techniques in terms of entropy, edge preservation index, contrast ratio, and Universal Image Quality Index (UIQI). This proposed technique increases contrast, while preserving the inherent characteristics of the original image, resulting in better quality histopathology images that can be used in disease diagnosis and inspection.
End-to-end adaptive object detection with learnable Retinex for low-light city environment

In the smart city context, efficient urban surveillance under low-light conditions is crucial. Accurate object detection in dimly lit areas is vital for safety and nighttime driving. However, subpar, poorly lit images due to environmental or equipment limitations pose a challenge, affecting precision in tasks like object detection and segmentation. Existing solutions often involve time-consuming, inefficient image preprocessing and lack strong theoretical support for low-light city image enhancement. To address these issue, we propose an end-to-end pipeline named LAR-YOLO that leverages convolutional network to extract a set of image transformation parameters, and implements the Retinex theory to proficiently elevate the quality of the image. Unlike conventional approaches, this innovative method eliminates the need for hand-crafted parameters and can adaptively enhance each low-light image. Additionally, due to a restricted quantity of training data, the detection model may not achieve an adequate level of expertise to enhance detection accuracy. To tackle this challenge, we introduce a cross-domain learning approach that supplements the low-light model with knowledge from normal light scenarios. Our proof-of-principle experiments and ablation studies utilising ExDark and VOC datasets demonstrate that our proposed method outperforms similar low-light object detection algorithms by approximately 13% in terms of accuracy.
Recurrent ALBERT for recommendation: A hybrid architecture for accurate and lightweight restaurant recommendations

Abstract The online recommendation system has benefited the traditional restaurant business economically. However, finding the best restaurant during rush time and visiting new places is tough. This objective is addressed through a restaurant recommendation approach, which impacts the human decision‐making method. With the help of collaborative filtering, some user‐based recommendation systems were designed to generate the best recommendation based on user choices. Thus, a user preferences‐based method is presented using A Lite Bidirectional Encoder Representations from Transformers and Simple Recurrent Unit to suggest restaurants based on user preferences. Here, a publicly available dataset from Kaggle called Kzomato is used with 9552 samples and 21 features. And the system obtained an F1‐score, precision, and recall of 86%, which will save time and provide the best recommendation based on user preferences easily.
An Application to Improve the Performance and Security in Cloud by File Division Technique

Cloud computing has transformed the modern business landscape by providing scalable, flexible, and cost-effective solutions for data storage and processing. However, relying on external cloud providers to manage and protect sensitive data has raised significant security concerns, namely the risk of data breaches. To address these issues and optimize system performance concurrently, this research project introduces a novel approach that combines data fragmentation and replication in the cloud environment. Data fragmentation involves breaking down large files into smaller fragments or pieces, each of which contains only a portion of the original data. The fragments are then dispersed across multiple cloud nodes to ensure that no single node stores an entire file. This fragmentation and replication strategy reduces the potential risk of data exposure during a security breach. Data replication across multiple nodes improves system performance in addition to providing security benefits. Replicating data fragments facilitates more efficient data distribution and retrieval, thereby reducing response times and enhancing overall system performance. This paper provides a comprehensive overview of the methodology employed in this approach, highlighting its fundamental concepts, potential benefits, and key security considerations, such as the importance of data isolation and encryption. By combining data fragmentation and replication, organizations can strengthen their cloud data security while improving system performance, providing a comprehensive answer to the challenges posed by cloud computing.
BiFrost: A Blockchain-Based Decentralized Messaging Application

BiFrost is a revolutionary solution for secure online communication and data storage. It addresses security concerns such as eavesdropping, man-in-the-middle attacks, and censorship by offering decentralization, immutability, and data security through the use of blockchain technology. User-submitted data are added directly to the blockchain, creating a global copy in each node, and only authorized users can access the data using private keys. The system is fully decentralized, meaning there is no central authority, making it immune to censorship and government oppression. The technology used in BiFrost includes IPFS, smart contracts, Ethereum, INFURA, Solidity, and SPF. BiFrost provides a secure and decentralized solution for online communication and data storage, eliminating the need for a trusted intermediary and ensuring the privacy and security of data.
Spot Pricing in Cloud Computing: A Comprehensive Survey of Mechanisms, Strategies, and Future Directions

Cloud computing (CC) has transformed the way businesses store, process, and access data, and spot pricing has emerged as a key feature of this technology. Spot pricing enables users to bid on unused computing resources at lower prices, providing cost-effective access to computing resources and helping businesses optimize their operational infrastructure expenses. The proposed research is focused on case studies and existing research based on the impact of spot pricing in CC, including its effect on workload distribution and resource allocation. It also emphasizes the significance of spot pricing in CC and its potential to benefit businesses of all sizes. The paper concludes with open research directions, including the benefits and challenges of spot pricing, the various factors to consider when using spot pricing, and the need for future research in this area highlighting its importance and potential impact on businesses.
Probabilistic and semantic descriptions of image manifolds and their applications

This paper begins with a description of methods for estimating probability density functions for images that reflects the observation that such data is usually constrained to lie in restricted regions of the high-dimensional image space—not every pattern of pixels is an image. It is common to say that images lie on a lower-dimensional manifold in the high-dimensional space. However, although images may lie on such lower-dimensional manifolds, it is not the case that all points on the manifold have an equal probability of being images. Images are unevenly distributed on the manifold, and our task is to devise ways to model this distribution as a probability distribution. In pursuing this goal, we consider generative models that are popular in AI and computer vision community. For our purposes, generative/probabilistic models should have the properties of (1) sample generation: it should be possible to sample from this distribution according to the modeled density function, and (2) probability computation: given a previously unseen sample from the dataset of interest, one should be able to compute the probability of the sample, at least up to a normalizing constant. To this end, we investigate the use of methods such as normalizing flow and diffusion models. We then show how semantic interpretations are used to describe points on the manifold. To achieve this, we consider an emergent language framework that makes use of variational encoders to produce a disentangled representation of points that reside on a given manifold. Trajectories between points on a manifold can then be described in terms of evolving semantic descriptions. In addition to describing the manifold in terms of density and semantic disentanglement, we also show that such probabilistic descriptions (bounded) can be used to improve semantic consistency by constructing defenses against adversarial attacks. We evaluate our methods on CelebA and point samples for likelihood estimation with improved semantic robustness and out-of-distribution detection capability, MNIST and CelebA for semantic disentanglement with explainable and editable semantic interpolation, and CelebA and Fashion-MNIST to defend against patch attacks with significantly improved classification accuracy. We also discuss the limitations of applying our likelihood estimation to 2D images in diffusion models.
Artificial intelligence-based language learning: illuminating the impact on speaking skills and self-regulation in Chinese EFL context

This study investigated the effectiveness of artificial intelligence-based instruction in improving second language (L2) speaking skills and speaking self-regulation in a natural setting. The research was conducted with 93 Chinese English as a foreign language (EFL) students, randomly assigned to either an experimental group receiving AI-based instruction or a control group receiving traditional instruction.The AI-based instruction leveraged the Duolingo application, incorporating natural language processing technology, interactive exercises, personalized feedback, and speech recognition technology. Pre- and post-tests were conducted to assess L2 speaking skills and self-regulation abilities.The results of the study demonstrated that the experimental group, which received AI-based instruction, exhibited significantly greater improvement in L2 speaking skills compared to the control group. Moreover, participants in the experimental group reported higher levels of self-regulation.These findings suggest that AI-based instruction effectively enhances L2 speaking skills and fosters self-regulatory processes among language learners, highlighting the potential of AI technology to optimize language learning experiences and promote learners' autonomy and metacognitive strategies in the speaking domain. However, further research is needed to explore the long-term effects and specific mechanisms underlying these observed improvements.
Cloud Cost Optimization: Model, Bounds, and Asymptotics

A Near-Optimal Capacity and Scheduling Policy for Cloud Users In “Cloud Cost Optimization: Model, Bounds, and Asymptotics,” Qu, Dawande, and Janakiraman study a long-term, dynamic resource-optimization problem for firms managing various cloud resources to process incoming computing tasks over time. Cloud resources vary in attributes, such as computing speed, memory, accelerators, and storage, tailored to different computing tasks, such as data warehousing, scientific computing, machine learning, and data processing. Firms can choose reserved resources for long-term commitments at lower costs or on-demand resources for flexibility at a higher price. Firms face three key trade-offs: the cost disparity between reserved and on-demand resources, the variation in resource attributes affecting performance and cost, and the challenge of balancing delay and resource costs. We propose an asymptotically optimal policy, demonstrating its effectiveness through a detailed numerical study based on Amazon Web Services data.
Robot Arm Control Using Hybrid intelligent

The design of a fuzzy logic control suffers from select parameters of the membership functions, scaling factors, defuzzification action, Inference engine and base rules. speaking generally, such prosecutions are executed by traditionally techniques which do not assure an robust fuzzy control system design. There are various techniques introduced in literatures that used Genetic Algorithms to optimize a fuzzy logic control component. . In this paper, the suggested control law consists of Fuzzy Logic Control (FLC) tuning via Geneticn Algorithm (GA). The FLC used because it is efficient tools for control of nonlinear and uncertain parameters systems. GA is mainly presented to find a simultaneous near optimum design of the membership functions, scaling factors, defuzzification Method, Inference engine and control base rules. GA with different fitness functions in a form of the cumulative response error which are widely used as an efficient optimization technique. This paper also introduce a new methodology with new multi-objective function to improve fuzzy control parameters based on Genetic Algorithm techniques. The dynamic model of the robot manipulator its done by differential equations, these equations are hardly nonlinear, parameters uncertainty and time varying with multiple input and multiple output (MIMO).The manipulator robot and the fuzzy genetic control are modeled in MATLAB SIMULINK; the manipulator robot model driven nonlinear controller to draw a circle in the space with and without parameters uncertainties. The proposed techniques showed that the proposed fuzzy controller gives superior response in the output performance. When the parameter uncertainties including in the system, given satisfactory response.
Turbulent Particle Swarm Optimization and Genetic Algorithm for Function Maximization

The optimization problems can be solved by using population based heuristic search techniques namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). One of the drawbacks of standard PSO was to prematurely converge on local optimal solutions. In this work, we have used Turbulent Particle Swarm Optimization (TPSO) instead of standard PSO due to the drawback mentioned above. Here, different operations of Genetic algorithm were included to obtain a good solution. In this paper, we would like to compare the results of both algorithms. Experimental results were examined with functions which were function maximization and results show that the Turbulent PSO outperform the GA.
A Framework for Extractive Text Summarization of Single Text Document in Tamil Language Using Frequency Based Feature Extraction Technique

Text summarization, a technique in Natural Language Processing, helps in summarizing documents like news articles, legal documents, essays and more. The content may be comprehensive and redundant. A summary gives an insight of the document. Text summarization is broadly classified into two categories—extractive and abstractive summarization. Abstractive summarization uses deep learning techniques to generate summary, just as humans generate summary using their own words and sentences. Extractive summarization highlights information based on some features or technique used to identify the importance of the sentence from the source document. Methods used for extractive summarization include ranking algorithms, sentence scoring, sentence similarity and so on. In this paper, a framework for extractive text summarization using features extracted from a Tamil document has been proposed. The summarizer is based on Fuzzy logic inference engine. The framework describes the modules involved in the generation of Extractive Text Summary for a single document.
BASiP: A Novel Architecture for Abstractive Text Summarization

The availability of information and news over the Internet is exploding. In this context, text summarization is becoming very important since it gives a good overview of the content. Also, it saves time by exposing the most significant information at a glance. Summarization techniques are very vital in extracting this useful information from lengthy text. In this work, a novel architecture for abstractive text summarization architecture, BASiP, has been proposed, which effectively generates a summary from the given text. The base model used for summarization is BART. The proposed architecture is compared with the existing work. It is found that BASiP performs well in terms of the ROUGE score. Also, a case study is given at the end to show the efficiency of BASiP, in generating a meaningful summary.
Evaluation of Hand-Crafted Features for the Classification of Spam SMS in Dravidian Languages

In this digital era, people are cheated in multiple ways by sending fake messages. Without realizing its impact, they respond to the links the cyber frauds share. This immediate reaction to the fraud messages makes people lose their balance in bank accounts or fall into some other horrible events. These types of fake or spam messages have to be identified earlier before they come to users' Inbox. This paper proposes a Spam message filtering model that extracts significant hand-crafted features and is classified using machine learning algorithms. This research collects 7700 short messages in Dravidian languages like Tamil, Kannada, Telugu, and Malayalam and creates an optimal Spam-Ham filtering framework. Experimentation has also been carried out with a benchmark dataset for performance comparison regarding accuracy, precision, recall, and F1-score.
Machine Learning-Based Model for Predicting Failure of Physical Machines in Cloud Computing

Cloud computing is a form of computing that makes use of the internet to provide a wide range of services and scalable computing resources. It is a commonly used technique because it enables the integration of software and hardware resources. However, when compared to traditional computer systems, the possibilities of letdown in a cloud-based system upsurge. One of the most important parts for achieving high dependability and accessibility for the cloud computing architecture is fault tolerance (FT). To achieve cloud computing reliability, failures must be investigated and rectified immediately. As a result, in this research, we investigated machine learning’s predictive capabilities by employing a variety of methods to improve the accuracy of failure prediction. We used machine learning (ML) and constructed a failure prediction model for forecasting physical machine (PM) failure in the cloud data center, as well as comparison-based testing on prediction accuracy. The procedures we considered are Support Vector Machine (SVM), K Nearest Neighbor (KNN), Gaussian Nave Bayes (GNB), and Random Forest. Precision, recall, F1-measure, and accuracy were used to measure the effectiveness of machine learning approaches. According to the simulation, KNN, SVM, and Random Forest performed well on the datasets.