Reviewerly/exHarmony huggingface dataset:
Semi-FLChain: Toward Secure and Efficient Semi-Supervised Federated Learning for IoT Using Blockchain

This paper proposes a novel semi-supervised federated learning (SSFL) algorithm for Internet of Things (IoT) systems using blockchain technology, called Semi-FLChain. The algorithm is designed to improve the efficiency and security of SSFL by combining hybrid data augmentation and Bayesian estimation to enhance the consistency regularization and pseudo-labeling methods in SSFL. The proposed algorithm aims to overcome the limitations of existing SSFL methods, such as the low accuracy of pseudo-labeling and the high computational cost of consistency regularization. The use of blockchain technology in Semi-FLChain further ensures the security and privacy of IoT devices and their data during the training process. The experimental results demonstrate that Semi-FLChain outperforms state-of-the-art SSFL algorithms in terms of both accuracy and efficiency, making it a promising solution for secure and efficient federated learning in IoT systems.
Certificateless Broadcast Searchable Encryption with Application to Cloud-Based Health Data Sharing

Cloud data sharing allows a sender to securely share data with a specific group of recipients through a cloud platform. To address privacy concerns, the data that is shared is usually encrypted and stored on the cloud server, which can make future retrieval challenging. To overcome this limitation, searchable broadcast authenticated encryption schemes have been introduced. These schemes enable the sender to authenticate and encrypt data for the intended recipients through the insecure and unreliable cloud platform. Only the user in the recipients set can perform keyword searches and access target data. However, certain existing schemes exhibit vulnerabilities such as keyword guessing attacks (KGA) and trapdoor misuse. Moreover, all of these schemes are based on either the public key certificate system or some identity base system, and these circumstances give rise to specific challenges, namely the complexities of managing certificates and the potential problems associated with key escrow. To tackle these challenges, we introduce a certificateless broadcast searchable encryption scheme that achieves ciphertext indistinguishability, trapdoor indistinguishability, and receiver anonymity. And we also eliminate the need for public certificates. Furthermore, we strengthen the security of our scheme by restricting search operations on the encrypted data to a designated server. We provide an extensive security analysis of our scheme and perform a thorough performance evaluation to showcase the practicality of the proposed scheme.
CNN-based Steganalysis Detects Adversarial Steganography via Adversarial Training and Feature Squeezing

Deep learning model's vulnerability and unreliability cannot be ignored. For example, adversarial steganography exploits adversarial examples to escape the recognition of CNN-based steganalyzer, which is a pressing issue for intelligent steganalysis algorithms. In this paper, we propose a steganalysis method that can effectively detect adversarial steganography via adversarial training and feature squeezing. Firstly, we train the steganalyzer based on the adversarial cover and stego images with small perturbations to improve the robustness of the steganalyzer, which can effectively identify small perturbations of samples. Additionally, the proposed steganalysis decreases the utilization space of adversarial attacks by squeezing the input samples' features throughout the detection phase. The results of our experiments demonstrate that the accuracy of detecting adversarial steganography may be effectively improved by the proposed approach.
A Blockchain-Based Federated Learning for Smart Homes

Smart home devices generate a substantial amount of local data, and finding effective ways to utilize this data while ensuring privacy has become an increasingly pressing concern. Technologies such as Smart Homes, Federated Learning and Blockchain offer promising solutions to address this challenge. We introduce a blockchain-based federated learning approach that leverages edge nodes to maintain a decentralized blockchain, thus mitigating the risks associated with single points of failure. Furthermore, this method utilizes local data from home IoT devices for model training, ensuring efficient learning while preserving data privacy. To address the challenges posed by non-independent and homogeneous data distribution, we propose a clustering method. This strategy effectively tackles the issues arising from non-homogeneous data distribution, consequently improving model accuracy. Finally, experimental results demonstrate that our proposed approach significantly enhances model accuracy and generalization while safeguarding user privacy.
Reference-Based Super-Resolution Reconstruction of Mural Images

To solve the problem of texture defect and content ambiguity of degraded mural images, in this paper, we propose a reference-based image super-resolution(RefSR) method, The repaired high-resolution mural images are used as reference images, and the multi-scale information of the images is used to establish the relationship between the reference images and the low-quality mural images. Aiming at the problems of color deviation and texture authenticity that may occur in the reconstruction process, an adaptive texture transfer module and a self-tuning pixel attention module are constructed; In addition, in order to reduce the complexity and applicability of the model, residual distillation module is used to reduce the training parameters of the model.
Convolutional Neural Networks for Abnormal Behavior Detection in a Special Place

How to detect security hazards in special places and achieve automatic detection of the anomalous behavior of people is a challenge for many companies. The traditional approach relies on manual inspections and video surveillance, which is less efficient. To address the drawbacks of the traditional approach, an automatic abnormal behavior detection method based on convolutional neural networks is proposed. In this study, the YOLOv3 algorithm in target detection is used to obtain abnormal behavior detection of people in surveillance videos based on the construction of a ResNet50 convolutional neural network model to achieve automatic detection of abnormal behavior. In order to verify the accuracy of the abnormal behavior detection, the Smoking and Calling Image Dataset from the Alibaba Tianchi dataset was used as the training set, and the joint supervised training of the model was realized. The experiments show that the system has good realtime performance and recognition accuracy and can meet the requirements of abnormal behavior detection in special places such as petrol stations.
An Innovative Similar Complaint Recommendation Model Integrating Semantic and Graph Embeddings

Artificial intelligence plays a vital role in driving the intelligentization of the complaint hotline 12345. To assist hotline operators in accurately understanding public demands and allocating them to municipal departments, we propose BERT-Graph that recommends historical complaint to pending complaint through a dual-tower recommendation model integrating semantic and graph embeddings. Since the complaint records are sparse, colloquial and similar complaint texts may transcribed semantically different by operators, we construct an event interact graph for the complaint text to avoid implying sole reliance on semantic information. We consider each complaint text as a node, the co-occurrence times of key words calculated by TF-IDF between texts as weighted edges, and we use the Graph Attention Network (GAT) to obtain graph embeddings. After obtaining the semantic embedding through pre-trained language model BERT, we concat it with the graph embedding and feed it to the dual-tower model. We add a self-attention module as the neural network in the dual-tower model and we use a fully connected layer to output the similarity score of the input texts. Experimental results on complaint hotline 12345 datasets of the Chinese city of Wuhan demonstrate that compared to other baseline models, BERT-Graph shows a significant improvement on the Mean Squared Error (MSE) metric, from 0.86 to 0.24.
Fast Artistic Style Transfer via Wavelet Transforms

Artistic style transfer aims to transfer artistic styles to the content images. Although artistic style transfer has received great attention in recent years, most existing style transfer methods are still limited in practical applications due to the slow transfer speed, such as difficulty in efficiently processing images with large scales (e.g., 1024x1024 pixels). To address this issue, we propose a fast artistic style transfer method based on wavelet transforms. Specifically, we utilize the wavelet transform to compress the content image and extract the low frequency component of the content image, which is an approximation of the content image and a quarter the size of the content image. We only stylize the low frequency component of the content image, which can effectively accelerate the style transfer. Experimental results demonstrate that our method can generate high quality stylized images with high efficiency.
Power Load Data Cleaning Method Based on DBSCAN Clustering and WGAN Algorithm

The various processes of acquisition and transmission of measurement data are subject to malfunction or interference, resulting in missing data. Traditional data restoration methods ignore the historical load change pattern and have low reconstruction accuracy. In this paper, we first use the DBSCAN clustering algorithm to detect outliers in the original load dataset and remove the outliers with large deviations to form a dataset with vacant values. Then, the Wasserstein distance is used to improve on the original GAN network. Through non-supervised training of WGAN, the neural network will automatically learn complex spatio-temporal relationships that are difficult to model explicitly, such as correlations between measurements and load fluctuation patterns. Finally, the authenticity constraint and contextual similarity constraint are used to optimize the hidden variables, so that the trained generator will be able to generate highly accurate reconstructed data. The algorithm analysis proves the performance stability of the proposed method, and the reconstructed data can reflect the real time characteristics of the measured data.
The Impact of the Web on Information Retrieval

chapter Share on The Impact of the Web on Information Retrieval Authors: Peter Mika Search about this author , Ricardo Baeza-Yates Search about this author Authors Info & Claims Linking the World’s Information: Essays on Tim Berners-Lee’s Invention of the World Wide WebSeptember 2023Pages 105–114https://doi.org/10.1145/3591366.3591377Published:05 September 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
impact of reading, asking, and paraphrasing on students' ability to write news item

This study aims to find out how students' ability to write news items is impacted by reading, asking, and paraphrasing strategies. The goal of the study was to determine whether the reading, asking, and paraphrasing strategies had a substantial impact on the students' ability to write news items. The SMA Negeri 2 Tg. Balai on Jl Sudirman served as the site for this study. The first-graders of the academic year 2015–2016 made up the study's population. 40 pupils served as the sample, while 320 students made up the pupulation. A research design known as experimental was used to carry out this investigation. Two distinct classes served as the research sample in the design. The control group was one of the classes, and the experimental group was the other. The control group received no treatment, whereas the experimental group received it using the reading, asking, and paraphrasing technique. A written test served as the research's primary tool. The t-test formula was used to determine the t-observed values for both groups as the foundation for testing the research's main premise. According to the study's findings, the tobs value was higher than the t-table value, which had a t-obs value of 3.28 and a t-table value of 2.02. Tobs &gt; Ttable (3.28 &gt; 2.02), as evidenced by this. The theory was approved. It suggests that the students' success in writing news items was significantly impacted by the use of the reading, asking, and paraphrasing strategies. Keywords: reading; asking; paraphrasing strategy; news item; writing
SEMIOTIC ANALYSIS OF THE CAT FOOD “WHISKAS” ADVERTISEMENT

This article is entitled “THE ANALYSIS SEMOTIC OF WHISKAS AN ADVERTISEMENT CAT FOOD” advertising is communication with the specific purpose of commerce a product or service. Semiotics is the science of signs. This research has the purpose of analysis semiotic in advertisement Whiskas. The analysis research meaning in semiotics and focuses more on and understands a sign and a sign when the sign is advertised Whiskas is a cat food brand that Whiskas has been producing the best cat food since 1958 and we always improve and guarantee the quality and quality of the best nutritional cat food throughout history. This research used the theory Ferdinand de Saussure system signifier and signified. The method employed in this analysis is descriptive qualitative with this Associate in there's an understanding of semiotics in advertisement Whiskas cat food. In the research analysis two questions are signs on advertisement Whiskas and characteristics of advertisement Whiskas. the data analysis takes in the website and YouTube.
Enhancing Next-Item Recommendation Through Adaptive User Group Modeling

Session-based recommender systems are increasingly applied to next-item recommendations. However, existing approaches encode the session information of each user independently and do not consider the interrelationship between users. This work is based on the intuition that dynamic groups of like-minded users exist over time. By considering the impact of latent user groups, we can learn a user's preference in a better way. To this end, we propose a recommendation model based on learning user embeddings by modeling long and short-term dynamic latent user groups. Specifically, we utilize two network units to learn users' long and short-term sessions, respectively. Meanwhile, we employ two additional units to determine the affiliation of users with specific latent groups, followed by an aggregation of these latent group representations. Finally, user preference representations are shaped comprehensively by considering all these four aspects, based on an attention mechanism. Moreover, to avoid setting the number of groups manually, we further incorporate an adaptive learning unit to assess the necessity for creating a new group and learn the representation of emerging groups automatically. Extensive experiments prove our model outperforms multiple state-of-the-art methods in terms of Recall, mean average precision (mAP), and area under curve (AUC) metrics.
An Architectural Review of Multi-Tenancy in Cloud Computing

Cloud computing has become a popular ingredient in the information management of enterprises cutting across the globe and size. Its popularity lies in its ability to provide resources – infrastructure, platform, and software – as services on demand without compromising much on the quality of service. On-demand resource provisioning is possible by sharing the resources across multiple users or multiple tenants. Among all the three (IaaS, PaaS, and SaaS) deployments, SaaS offers more cost benefit to enterprises and users as compared to its predecessors because of its ability to share both hardware and software across multiple tenants with very minimal data sharing. The feature of sharing resources among multiple users known as multi tenancy is essential in achieving the objectives of cloud computing. This paper gives a detailed account of multi tenancy in cloud computing with special focus on SaaS applications and its associated problems followed by solution approaches.
An Analysis of Deep Learning Techniques Adopted in Medical Imaging

The various methods which have been adopted in processing and segmentation of medical images using deep learning and machine learning techniques are examined and analyzed in this article. Medical images analysis and their methods have been swiftly evolved into deep learning techniques and especially in convolutional neural networks. Deep learning concepts that may be used for the image classification, detection, and logging of medical related pictures and objects have been examined. Medical applications include: research and investigations into neuro, retinal and pulmonary, digital pathologies, breast, heart and musculoskeletal diseases and their corresponding analysis. Deep learning has already been used to accurately diagnose diseases and classify image samples, and it has the potential to revolutionise the entire landscape of healthcare. These uses are only expected to expand in the future. The most recent developments, including a critical analysis of the current problems have been summarized, and made plans for additional research in medical imaging.
Framework of IoT, Blockchain, Digital Twins, and Artificial Intelligence Solutions in Support of the Digital Business Transformation of Logistics 5.0

In organizations, it is vital to reach the customer. For this reason, logistics is considered an essential function in organizations. A general definition of logistics includes planning, production, purchasing, transportation, storage, loading and unloading, handling, packaging, processing, distribution, control, information processing, and traceability. These activities must be balanced to achieve the primary objectives of logistics. This situation requires an appropriate flow of goods/services/people/information at the right time with the right equipment/devices/competent people at the right place in the right quality and quantity to satisfy customers. Logistics should also be sustainable, flexible, and resilient.
Blockchain-Based Decentralized Information Sharing for Supply Chain Management

Entities in modern supply chains are geographically dispersed across the globe. These entities have to collaborate to function as an integrated system. Advancements in technology facilitate various means for effectively sharing information among supply chain entities. At present, organizations are IT-enabled, and they use dedicated information systems to manage and share their information. In fact, the majority of such systems are technically centralized in nature. Centralized information systems are exposed to a set of security vulnerabilities, and they can cause trust issues in supply chains. Decentralized information sharing can serve several benefits to supply chains. Blockchain is an emerging database technology that facilitates decentralized information sharing. Blockchain-based decentralized information sharing can bring attractive benefits for supply chain stakeholders. This chapter narrates the importance of decentralized information sharing in supply chains and introduces blockchain technology and its opportunities in the stream of supply chain management.
Internet of Things Applications Architecture for Industrial Interoperability Business Service

Wireless sensor data communication networks (WSNs) based on the Internet of Things (IoT) technology have many potential applications for the industrial world. The IoT paradigm encompasses ubiquitous computing, pervasive computing, data communication protocols, sensing technologies, and embedded devices, and they merge selectively to form an information system where the physical and digital worlds meet. They can serve different categories of business services through continuous symbiotic interactions. In this way, the formed interconnected information system, through the Internet, exchanges data and information to create services that bring tangible benefits to the industrial world and its supply chain operations. This chapter presents a system architecture (i.e., Apparel Business Decentralized Data Integration (ABDDI)) and its knowledge representation scheme, based on formal languages – Description Logics (DLs), to address the needs of formal information modeling and reasoning for web-based services provision using ontology. Finally, the chapter presents an example of the experimental result of service composition ontology's similarity assessment.
The Effects of Metaverse and Blockchain-Based Cryptocurrencies on Social Life

Due to the necessity of living conditions and the desire to seek alternative living environments, people have recently focused on virtual worlds. The purpose of this chapter is to provide information about what kind of activities people can do in Metaverse virtual worlds without being dependent on mediator platforms in a decentralized way. Moreover, information is given about the advantages of blockchain-based Web 3.0 technology in virtual environments. The advantages of integrating virtual reality technology into Metaverse virtual worlds and properties of non-fungible tokens are explained. In addition, the most popular Metaverse projects in the crypto coin markets are mentioned with the effects of these projects on society discussed.
A Major Transition in the Evolution of Human Cooperation Since the Emergence of Agriculture

The chapter describes how the switch toward a decentralisation of data storage can potentially advance the human economy and society to a new, unprecedented level as not seen since the origin of agriculture. Starting from the inception of the internet, its related I.T. developments are recalled going from a truly open and collaborative circumscribed network to the gradual establishment of new network nodes. During this time, I.T. corporations have profited greatly becoming larger and larger and making the internet much more centralised. However, thanks to the blockchains, there is now the possibility to go partly back to a more “liberal” state of the networks in which more weight is given back to minor network nodes. In this manner, human cooperation would increase enormously.
Impact and Use of Blockchain Technologies in Organizations

Blockchain consists of emerging technologies such as IT infrastructure, cloud computing, security, peer-to-peer network, and cryptographic keys. The major objective of the chapter is to demystify the technologies which are supporting the blockchain applications. The IT infrastructure enhances the blockchains by coordinating and maintaining the infrastructure to work on the security and cloud storage. The blockchain can be termed as a concept which is more secure among the relevant technologies. But still there exists the chance of an attack. The security is often implemented in the network of blockchain. This security prevents the risk of cyber-attacks and fraud practices in the network. Peer-to-peer is known as a type of computer network which is frequency implemented for the digital media file distribution. In this peer-to-peer network each node would be acting as a server as well as a client by receiving and supplying the files.