Reviewerly/exHarmony huggingface dataset:
A Decentralized Cooperative Navigation Approach for Visual Homing Networks

Visual homing is a lightweight approach to visual navigation. Given the stored information of an initial “home” location, the navigation task back to this location is achieved from any other location by comparing the stored home information to the current image and extracting a motion vector. A challenge that constrains the applicability of visual homing is that the home location must be within the robot’s field of view to initiate the homing process. Thus, we propose a blockchain approach to visual navigation for a heterogeneous robot team over a wide area of visual navigation. Because it does not require map data structures, the approach is useful for robot platforms with a small computational footprint, and, because it leverages current visual information, it supports a resilient and adaptive path selection. Further, we present a lightweight proof-of-work mechanism for reaching consensus in the untrustworthy visual homing network.
Quantum Resistance Group Key Management for IoTs

Group communication plays a vital role in our interconnected society, facilitating efficient and secure data exchange among multiple endpoints. The issue pertains to the intricate nature that emerges within the limited settings of the Internet of Things and sensor networks. GKM plays a crucial role in maintaining security in group communication schemes. Existing GKM schemes commonly rely on public-key cryptography, which may be susceptible to potential threats posed by quantum computers. This study investigated the usage of post-quantum GKM schemes in the realm of IoTs. To enhance communication security and efficiency in the era of quantum computing, we made significant progress in implementing a post-quantum GKM scheme. Specifically, we have chosen the SABER Key Encapsulation Mechanism, which is one of the four finalists in the National Institute for Standards and Technology (NIST) process. We also present preliminary simulation results for our scheme.
A Neuron Attention-Based Convolutional Neural Network for Intra Luma Quality Enhancement of H.266/Versatile Video Coding

Intra prediction plays a critical role in eliminating spatial domain correlation in video coding. To further enhance the accuracy of intra prediction, we propose a Neuron Attention-based Convolutional Neural Network (NACNN) to improve the luma quality of the current coding tree unit (CTU). The NACNN reasonably considers the influence of different quantization parameters on video coding and introduces a lightweight multi-scale neuron attention mechanism to enhance the quality of the reconstructed CTUs while simplifying network complexity. Experimental results demonstrate that the proposed method can achieve average BD-rate savings of 1.24%, 0.34% and 0.16% for Y, Cb and Cr, respectively, compared with H.266/Versatile Video Coding (VVC).
Targeted Ads Analysis: What are The most Targeted Personas?

In recent years, the handling of personal information in web advertisements have been regulated and updated frequently. The algorithms for targeted advertisements delivered based on users’ browsing history and search results is opaque, and hence there is a concern of invasion of privacy. Therefore, we have four research questions as a survey of targeted advertisements by cookie information. Which personas are most likely to be targeted? Which websites are most frequently targeted? How long are they displayed? Is automatic observation possible? To answer these questions, we developed a system that obtains automatically transitions websites and targeted advertisement URLs for each persona using Selenium, a portable framework. We report the development of the system and the results of our experiment.
Siamese Network with Hybrid Fusion Strategy for RGB-T Tracking

Visual object tracking is to capture the target according to the initial target state, which can be widely used in autonomous driving, human interaction, smart surveillance. In the community of computer vision, the subject has become more popularized. With development of multi-modality vision, RGB-T tracking has been focused by researchers due to its robust performance and wider applications. Existing methods mainly focus on data fusion, which can be categorized into three manners, including image fusion, feature fusion and decision fusion. In this paper, we aim to analyze the complementary role of various fusion types. We proposed a hybrid fusion strategy, which combines those types into a framework. We select Siamese based tracker as baseline and evaluate them in GTOT dataset. Our fusion strategy obtains the best performance against all other fusion modules, which achieves 65.3% and 77.8% success rate and precision rate. Our method can validate the complementary role of different fusion methods.
Investigating the Effectiveness of Deep Learning and CFA Interpolation Based Classifiers on Identifying AIGC

AIGC is content that is generated by any AI framework. In recent years, the ability to generate AIGC has become more accessible to members of the public. This poses a threat, as AIGC has become indistinguishable to the human eye, and could be used to spread misinformation. In this paper, we investigate the ability of three deep neural networks to identify AIGC. We also propose two classifiers that use CFA Interpolation related approaches that identify if images contain genuine levels of interpolation error to identify AIGC. Our models were trained and tested on their ability to classify both previously seen and unseen image subjects to test their generalizability. It was found that CNNs performed the best at this task, but the CFA models also produced reasonable results.
Research on the Application of Computer Image Vision Technology in the Curriculum System of Preschool Education

An interactive learning system based on intelligent image recognition for young children is proposed. The platform consists of cloud server and learning platform. The learning machine is connected to the cloud server through the Internet or WIFI, and the cloud server includes a cloud service module, a cloud data module, an information comparison module and an information feedback module. The learning machine includes an image scanning module, an image acquisition module, a central processing unit, an image display module, a 3D virtual model modeling module, a 3D scene design module and an interactive roaming experience module. A voice-based interactive system for young children is established. It can promote children's development of language goals. Children's interest in language can be increased through timbre, language input, language conversion, phonetic word conversion and other methods. It can build a "cognition" model in children's brains, enabling children to distinguish the differences between similar things, thereby improving children's cognition level.
Recommendation System for Research Grants

This research paper proposes the design and implementation of a recommendation system for researchers to simplify the process of finding research grants. Despite the positive impact that researchers and scientists have on the world through their discoveries and innovations, securing funding for their projects remains a challenging and time-consuming task. This paper objects to address the issue by introducing a recommendation system that leverages the architecture of the system, detailed design, project planning, and requirements to suggest grants that researchers may find interesting based on their platform activities. The proposed system will be beneficial for researchers, students, and scientists seeking grants, as it will provide an efficient and user- friendly way of finding grants that fit their interests and project requirements.
Denoising of Aged Album and Mural Photographs Using GAN Techniques

This particular study introduces a GAN (Generative Adversarial Network) based methodology to denoise historic photographs. Different forms of Speckle., some form of Gaussian impurity., Salt and Pepper noise., frequently damages the historical photographic clarity. These kinds of noise might make it harder to see crucial aspects of an image and impede additional image processing. We provide a denoising approach based on a GAN to solve this problem. These types of GAN's are used to generate denoised image from a noisy input after being trained to learn the underlying clean image distribution. The testing findings show that the suggested strategy may successfully eliminate various types of noise from old photographs and murals and enhance the quality visually. The outputs demonstrate that the suggested methodology outperforms the other denoising techniques based on the visual quality and denoising efficacy. We have used a total of 550 samples on both the GAN models. For Pix2Pix GAN., we managed to achieve PSNR of 31.295., MSE of 0.571 and SSIM of 0.613 at 500 epochs. GFP GAN., a pre-trained model., was able to obtain PSNR of 33.580., MSE of 0.057 and SSIM of 0.892. This work offers a viable method for denoising old photos and could make an impact on many different image processing applications.
Content-Based Image Retrieval for Remote Sensing Images using Hybrid Firefly and Grey Wolf Optimization Algorithm

In recent years, Content-Based Image Retrieval (CBIR) has rised as an advanced approach for identifying and retrieving images from a database based on their visual content. CBIR analyzes visual material primarily using low-level properties such as color, texture, and shape. These qualities, however, may fall short in terms of capturing high-level semantics and contextual information, limiting the system's ability to fully understand the whole significance of images. To address this limitation, the hybrid Firefly and Grey Wolf Optimization (FGWO) algorithm has been proposed, which combines the Grey Wolf Optimization (GWO) with the Firefly Algorithm (FA). The FA is a nature-inspired swarm intelligence-based optimization method inspired by firefly flashing light behavior. This integration improves the image retrieval capabilities of the system. Furthermore, an Adjusted Intensity Based Variant of Adaptive Histogram Equalization(AIBVAHE) filter is utilized to efficiently reduce noise in satellite images. The AIBVAHE filter improves visual contrast by redistributing intensity levels, resulting in a more uniform histogram. Color Moments and Local Binary Pattern (LBP) are utilized as feature extractors in the hybrid feature extraction approach, efficiently extracting the most relevant features suited for Content-Based Satellite Image Retrieval (CBSIR) systems. When compared to existing methods, the experimental findings show that this methodology is successful, with an impressive accuracy of 98.91%, precision of 98.41%, and recall of 98.50%.
Big Data Mining And Clustering Using Distributed Bayesian Matrix Decomposition

Technological advancement has enabled us to store and process massive amounts of data in a much shorter period of period. The phrase "Big Data" merely describes the enormous amounts of data that are now regularly utilized in business and academic settings. The emphasis here is not only on data collection, but also on careful data analysis in order to obtain meaningful results. There are several approaches to dealing with massive data streams. Data clustering into compact units is one such method. This not only reduces data size but also allows it to be used more efficiently. Because all of these devices are from different manufacturers, the data generated is heterogeneous in nature and contains noise. A fundamental problem is gaining knowledge from heterogeneous, noisy data. Several strategies for mining knowledge from big data have already been proposed. Because big data is frequently acquired from dispersed locations and stored on multiple machines, handling these extremely large data sets in a single machine is either inefficient or impossible. We provide a distributed Bayesian matrix decomposition model (DBMD) for big data mining and clustering. Statistical inference, alternate direction method of multipliers (ADMM), and accelerated gradient descent (AGD) are the three techniques utilized to implement distributed computing. Investigated are the theoretical convergence tendencies of various approaches. We offer a perfect plug-in weighted average that minimizes estimate variance to get over noise heterogeneity. In real-world experiments, our algorithms outperform or are on par with two popular distributed approaches, Scalable- nonnegative matrix factorization (NMF) and Scalable- k-means++, which are confirmed by our theoretical results from synthetic studies.
Federated Learning for Sparse Principal Component Analysis

In the rapidly evolving realm of machine learning, algorithm effectiveness often faces limitations due to data quality and availability. Traditional approaches grapple with data sharing due to legal and privacy concerns. The federated learning framework addresses this challenge. Federated learning is a decentralized approach where model training occurs on client sides, preserving privacy by keeping data localized. Instead of sending raw data to a central server, only model updates are exchanged, enhancing data security. We apply this framework to Sparse Principal Component Analysis (SPCA) in this work. SPCA aims to attain sparse component loadings while maximizing data variance for improved interpretability. Beside the l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> norm regularization term in conventional SPCA, we add a smoothing function to facilitate gradient-based optimization methods. Moreover, in order to improve computational efficiency, we introduce a least squares approximation to original SPCA. This enables analytic solutions on the optimization processes, leading to substantial computational improvements. Within the federated framework, we formulate SPCA as a consensus optimization problem, which can be solved using the Alternating Direction Method of Multipliers (ADMM). Our extensive experiments involve both IID and non-IID random features across various data owners. Results on synthetic and public datasets affirm the efficacy of our federated SPCA approach.
LLM-TAKE: Theme-Aware Keyword Extraction Using Large Language Models

Keyword extraction is one of the core tasks in natural language processing. Classic extraction models are notorious for having a short attention span which make it hard for them to conclude relational connections among the words and sentences that are far from each other. This, in turn, makes their usage prohibitive for generating keywords that are inferred from the context of the whole text. In this paper, we explore using Large Language Models (LLMs) in generating keywords for items that are inferred from the items’ textual metadata. Our modeling framework includes several stages to fine grain the results by avoiding outputting keywords that are non-informative or sensitive and reduce hallucinations common in LLM’s. We call our LLM-based framework Theme-Aware Keyword Extraction (LLM-TAKE). We propose two variations of framework for generating extractive and abstractive themes for products in an E-commerce setting. We perform an extensive set of experiments on three real data sets and show that our modeling framework can enhance accuracy-based and diversity-based metrics when compared with benchmark models.
Music Marketplace Application Using Non Fungible Tokens

The adoption of a music player that makes use of blockchain technology has the power to fundamentally alter the music business. Music players may guarantee safe and open music ownership and distribution by using a decentralized ledger architecture. Long-standing problems in the music business include piracy, unauthorized distribution, and underpaying musicians. By making it possible for a tamper-proof ledger system that monitors music ownership and distribution, blockchain technology can provide a resolution to these problems. In a music player powered by blockchain, each track would have a unique digital signature, and ownership information would be stored on the blockchain. When a user streams or downloads a song, the payment would be automatically distributed to the appropriate parties, including the artist, record label, and other contributors, based on pre-programmed smart contracts. This ensures that all parties involved in the production and distribution of music are fairly compensated for their contributions. Furthermore, blockchain technology can provide transparency in royalty payments, which has been a major issue in the music industry for decades. By tracking music plays and distributing payments automatically through smart contracts, blockchain-based music players can ensure that artists receive the royalties they are entitled to, and reduce the risk of disputes. This means that artists no longer have to rely on record labels to accurately report their earnings, and can have more control over their own revenue streams. Overall, the implementation of a blockchain-based music player has the potential to create a more equitable and transparent music industry. By utilizing a tamper-proof ledger system, blockchain technology can provide secure and transparent music ownership and distribution, while also ensuring fair compensation for artists and other contributors. As the technology continues to evolve, it will be interesting to see how blockchain-based music players will impact the music industry, and how they will shape the relationship between artists and their fans.
Empathetic Dialogue Generation with Contrastive Learning and Multi-Source Information Fusion

An empathic dialogue generation model based on comparative learning and multi-source data fusion is proposed to address problems of the under-extraction of contextual emotional expressions in discourse and limited empathic response diversity in current empathic dialogue generation tasks. The model introduces cognitive, affective, and dialogue intention information to guide dialogue generation. In the emotion classification stage, contrastive learning is used to capture the emotion information of the contextual discourse and to improve the contextual discourse representation. In the empathic response generation phase, the multi-attention mechanism fuses multiple sources of cognitive, emotional, and dialogue intention information, capturing more in-depth semantic and emotional information in the dialogue. Experimental results on the public dialogue dataset ED show that the proposed model can fully exploit cognitive, affective, and dialogue intent information to improve classification accuracy and generate more empathetic dialogue responses.
Integrating Security into the Software Development Life Cycle: A Systematic Approach

Security management is a critical aspect of secure software development that plays a crucial role in safeguarding applications and protecting sensitive data. This paper provides an overview of the security management process and its significance in ensuring the development of secure software. By adopting a robust security management process, organizations can proactively identify and address security vulnerabilities, mitigate risks, and enhance the overall security posture of their software products. This paper may help software developers, security practitioners, and organizations aiming to establish an effective security management process within their software development lifecycle.
A Teaching and Learning Based Optimization Algorithm for Efficient Routing in Vehicular Ad Hoc Networks

Many technologies offered by Intelligent Transportation Systems (ITS), such as encrypted financial transactions, media applications, and effective traffic control, depend on Vehicular Ad Hoc Networks (VANETs). Vehicular network topology is always changing as a result of increased traffic, and network scalability is limited by sparse network dispersion. As a result, maintaining a steady, ideal path is difficult in the network, which increases instability. This study presented Teaching and Learning based Optimisation (TLO), which generates optimal routing in vehicular communication. The results were compared with existing techniques including the Whale Optimisation Algorithm (p-WOA), Harris Hawks Optimisation (HHO) and Multi-Objective Optimisation Algorithm (MOOA). The proposed technique achieved better results in terms of number of Number of alive nodes, including Latency, and Throughput respectively.
Unleashing the Power of Large Language Model, Textual Embeddings, and Knowledge Graphs for Advanced Information Retrieval

Acquiring knowledge beyond the usual expertise is a critical challenge when implementing semantic information solutions for querying a knowledge base. To address this difficulty, one proposed solution was to use knowledge graphs in conjunction with traditional Question & Answering (Q&A) systems. However, this approach struggles with limited facts, difficulty in obtaining further insights into the context, and limited ability to handle complex questions, leading to inaccurate or irrelevant answers. To overcome these limitations, we present an approach for answering inference-based questions that integrates knowledge graphs, a large language model, and relevant embeddings from a vector database. Combining knowledge graphs and word embeddings significantly enhances the strength of both techniques, leading to improved performance of Question and Answering systems. We begin with generating representations of the relevant nodes in the knowledge graph and retrieve the most appropriate information from a collection of stored textual data using word embeddings. This approach tackles the shortcomings of conventional approaches that rely solely on knowledge graphs and are too rigid to handle the nuances of the context. This method provides a sophisticated understanding of language and context, enabling it to handle complex questions that may involve multiple entities and relationships with a better understanding of the facts and context in which the question is being asked. The system's ability to handle complex queries is evidenced through a combination of theoretical analysis and empirical data. Our approach has demonstrated exceptional efficiency on a benchmark dataset, as evidenced by evaluating the F1 score.