Reviewerly/exHarmony huggingface dataset:
E-Commerce Recommender Systems: A Comprehensive Survey

Recommender systems, systems designed to predict a user's preferences or evaluations for a product, have become integral to e-commerce applications in recent years. These systems are prevalent on popular e-commerce websites that sell products, books, and music. Beyond e-commerce, recommender systems are also utilized for various other sectors such as restaurants, humor, life insurance, financial services, and even Twitter followers. The development of the web and recommender systems has been a simultaneous process, with each influencing the other. To enhance the quality of the recommendation process, these systems are now incorporating social information. Recommended systems use local, implicit, and personal data collected from the Internet to refine their processes for future improvements. This study aims to review and summarize the latest developments in the techniques used by e-commerce recommender systems. Moreover, the review of the recent literature has identified key findings, significant research gaps, and notable drawbacks in the development and application of these systems. The objective is to provide a comprehensive picture of the current state of the field and indicate potential areas for future research, to contribute to the growth and sophistication of recommender systems.
Single Image Dehazing Based on Deep Feature Fusion Network

Image dehazing can make images clearer and more realistic. The dehaze image is more easily recognized and understood by the human eye, and also helps to improve the performance of the computer vision system. Transformer has found significant application in image dehazing because of its remarkable ability to extract global features. However, employing Transformer solely in image dehazing often leads to the loss of image details, while CNN is ineffective in global feature extraction. Therefore, we integrated CNN and Transformer to enhance image dehazing. We introduced a novel technique to address the feature fusion dilemma by utilizing a transmission-guided map to extract haze-based photo characteristics, thereby uniting the features of CNN and Transformer. The combined features contain both image nuances and extensive-range data. Comprehensive tests indicate that our approach outperforms all others on numerous quality assessment metrics.
Fuzzy Logic Controller for Autonomous Environment-Based Drone Swarm Formation

The study primarily delves into the development of a fuzzy logic-based controller for adaptive drone swarm formation control taking into account the velocity, altitude, distances, and approached terrain qualities perceived by the system. As such, it made use of the Mamdani framework whose sets of rules were developed through the use of MATLAB - primarily focusing on the aforementioned factors in order to devise a controller that would allow for terrain-based adaptive formation control which would be of great benefit in the operation of drone swarms traversing dynamic terrain systems. In this regard, the study was then able to develop a fuzzy logic controller that exhibits the interaction between these factors whereby depending on the initial speed of the drone swarm as it approaches the terrain in conjunction with its width, the formation of the drone swarm shall then be transformed or adjusted accordingly.
A Study on Efficient Image Classification of Historical Monuments Using CNN

With the rise of tourism and data democratization, among many classification issues is the recognition of landmarks in the field of vision and perception, which is being actively researched. After spending so many years classifying structures and monuments in general from photographs, fine-grained challenges are now the subject of attention. This research paper presents an investigation into the classification of Indian monuments using a convolutional neural network (CNN), experimenting with ResNet50, InceptionResNetV2, EfficientNetB1, EfficientNetB3, and MobileNetV2. The experimented model was trained on a dataset of 24 different types of Indian monuments, consisting of over 4,000 images, with the goal of accurately classifying new images of Indian monuments based on their type. Various hyperparameters such as batch size, learning rate, and epochs were tuned to enhance the accuracy of the model. Additionally, data augmentation techniques, for instance random rotations, zooming, and flipping, were utilised on the training photos to increase the model's resilience. The trained model achieved an accuracy of 13% from the ResNet50 model to over 98% from the MobileNetV2 model (training accuracy), demonstrating the effectiveness of the final architecture and the tuned hyperparameters for the classification of Indian monuments.
Embedded Software Implementation of Privacy Preserving Matrix Computation using Elliptic Curve Cryptography for IoT Applications

Security concerns about data collected and processed by network-connected electronic devices has motivated the need for privacy-preserving computation. One of the most promising theoretical tools in this direction is homomorphic encryption. In this work, we explore the efficient implementation of elliptic curve-based additively homomorphic encryption which allows certain matrix-vector operations in the encrypted domain without the prohibitive computational complexity of fully homomorphic encryption. We present an algorithmic framework for the same, supported by detailed performance analysis of our software implementation evaluated on an embedded microprocessor along with memory-time trade-offs and side-channel countermeasures. We also discuss various applications which can benefit from this privacy-preserving compute framework.
Benchmarking for Integrating Logic Rules with Everything Else

Integrating logic rules with other language features is increasingly sought after for advanced applications that require knowledge-base capabilities. To address this demand, increasingly more languages and extensions for such integration have been developed. How to evaluate such languages? This paper describes a set of programming and performance benchmarks for evaluating languages supporting integrated use of rules and other features, and the results of evaluating such an integrated language together with logic languages and languages not supporting logic rules.
Denoising Diffusion Adversarial Models for Unconditional Medical Image Generation

Unconditional medical image synthesis is the task of generating realistic and diverse medical images from random noise without any prior information or constraints. Synthesizing realistic medical images can enrich the quality and diversity of medical imaging datasets, which in turn, enhance the performance and generalization of deep learning models for medical imaging. Prevalent approach for synthesizing medical images involves generative adversarial networks (GAN) or denoising diffusion probabilistic models (DDPM). However, GAN models that implicitly learn the image distribution are prone to limited sample fidelity and diversity. On the other hand, diffusion models suffer from slow sampling speed due to small diffusion steps. In this paper, we propose a novel diffusion-based method for unconditional medical image synthesis, Diff-Med-Synth, that generates realistic and diverse medical images from random noise. Diff-Med-Synth combines the advantages of denoising diffusion probabilistic models and GANs to achieve fast and efficient image sampling. We evaluate our method on two multi-contrast MRI datasets and show that it outperforms state-of-the-art methods in terms of quality, diversity, and fidelity of the synthesized images.
Accelerating Block Propagation with Sender Switchover in a Blockchain

In a public blockchain, the block propagation time has a significant impact on the performance, security and fairness of mining. Reducing the propagation time can increase the transaction processing performance, reduce the fork rate, and increase security. We propose a method to improve block propagation with block sender switchover, even if a node is receiving a block. The method is not vulnerable to eclipse attacks because the neighboring nodes are not changed. Our simulation shows that the proposed method improves the 90th percentile value of the propagation time by up to 18% and the fork rate by up to 7.9%.
Tool for Semi-Automatic Segmentation and Labeling of Regions of Interest on Mid-Lateral Oblique Screen-Film Mammograms

Artificial intelligence (AI) algorithms have enormous potential applications and cover the entire medical imaging lifecycle, from image creation, diagnosis, classification and even prediction of particular pathologies. A problem for the development and clinical application of AI algorithms is the limited availability of extensive and representative training data including expert labeling (Manual Labeling). Current supervised AI methods require a data pre-processing stage for labeling data to train, validate and test algorithms with an optimal approximation to the ground truth. In this paper, a graphical interface was developed and implemented to label in a semi-automatic way Regions of Interest of Mid-Middle Lateral Oblique Screen-Film Mammography (MMLOSFM) through pre-processing techniques such as segmentation and image enhancement. Based on the analysis performed using four structural comparison indexes, it was demonstrated an efficient segmentation and labeling of MMLOSFM with a consistently high similarity (91.6% compared to the ground truth provided by the MIAS database).
Supervised Machine Learning: Discriminative Learning Methods for Pattern Classification using HELM

The ability to forecast the data is significantly helped by machine learning's involvement. At the present time, data analysis is playing a very significant part in both the Information Technology (IT) industry and academic institutions. Numerous real-time applications, such as translation in real time, facial recognition, musical compositions, and so on, have been developed through the various initiatives that have been carried out up to this point. According to the standards that must be met by every data analyst, data must first be preprocessed before any predictions can be made on that data. Predictions must also be based exclusively on the preprocessed data. This work demonstrates the data pre-processing along with the prediction of a churn model. The model contains a set of records of various customers, including information regarding whether or not the client possesses credit cards and whether or not the consumer is an active member. This research works on the construction of a hybrid ensemble learning model (abbreviated HELM) to collect heterogeneous weak learners in order to anticipate such analyses. In order to work on the Churn Model classification, a variety of machine learning algorithms are integrated with one another. The findings revealed the efficiency of each machine learning algorithm that was used, in addition to HELM's operational effectiveness.
Recycling for Recycling: RoI Cropping by Recycling a Pre-Trained Attention Mechanism for Accurate Classification of Recyclables

Automated classification of recyclable waste is necessary to process a huge amount of recyclables for reuse. This research features recycling a pre-trained attention mechanism for cropping region of interest (RoI) for efficient classification of recyclable waste. We report that an attention mechanism pre-trained with the MNIST dataset, followed by simple morphological operations, successfully provides a bounding box for a recyclable object to be fed into object recognition models such as ResNet50 and EffNetB0. This way, we avoid the cost of annotating large datasets to train state-of-the-art object detection models such as YOLO and R-CNN. Experimental results using the Recyclable Solid Waste Dataset (RSWD) show that our attention-based RoI cropping method is effective enough to separate an object for recognition to achieve accurate classification of recyclables.
Improved Red Deer Algorithm for Scientific Workflow Scheduling in Cloud Environment

One of the most difficult problems in the technological sector is the versatility of cloud computing, which can offer a flexible and responsive infrastructure in the realm of information technology. A significant problem in cloud computing that is addressed in recent research is workflow scheduling. With the quick advancement of cloud computing, scheduling the intricate scientific process on the cloud has grown to be an incredibly difficult task. It has been identified that one of the key challenges to optimizing the performance of cloud computing is scheduling workflow tasks so that it is processed by the most effective cloud network resources. Meta-heuristic optimization algorithms are frequently utilized to find a solution to effective task scheduling because of the intricacy of the problem and the extent of the search space. This research suggests a unique Improved Red Deer Algorithm (IRDA) to shorten execution time subject to a set budget. The performance of the proposed algorithm is evaluated against conventional optimization algorithms by applying it to scientific workflows including Montage and Epigenomics. The experimental results reveal that the proposed algorithm works better than other compared methods in reducing workflow task execution time and cost.
A Semi-automatic Approach for Enhancing the Quality of Automatically Generated Inventories

Inventories are precious sources of information for security-related processes. As a consequence, the quality of the data in the inventories plays a crucial role in the overall quality of the fed processes. This paper takes this challenge and provides heuristics to improve the accuracy of automatically generated inventories through a semi-automatic approach leveraging user knowledge.
Towards Automated Cyber Range Design: Characterizing and Matching Demands to Supplies

Cyber ranges mimic real-world cyber environments and are in high demand. Before building their own cyber ranges, organizations need to deeply understand what construction supplies are available to them. A fundamental supply is the cyber range architecture, which prompts an important research question: Which cyber range architecture is most appropriate for an organization's requirements? To answer this question, we propose an innovative framework to specify cyber range requirements, characterize cyber range architectures (based on our analysis of 45 cyber range architectures), and match cyber range architectures to cyber range requirements.
A Hybrid 2D-1D CNN for Scanner Device Linking Based on Scanning Noise

Ensuring the authenticity of scanned documents is of major concern, these ones being often admitted as evidence by organizations. “Is there any way to verify that a document was scanned by a device without having access physically to the source device itself?” is a wide-open question. In this paper, we aim at answering it in the affirmative by means of the first data-driven hybrid machine learning framework that compares image noise features to check if two documents have been digitized with the same scanner or not. Such a problem is known as device linking function. Different comparative experiments conducted on the same and different scanner models on a broad set of administrative documents demonstrate that our method is efficient in linking scanned images even if scanner devices are unknown to the investigator. Our success rate of 96% appears to be the novel state of art reference in such application domain.
Federated Topology Preserving Domain Adaptation for Human Activity Recognition

Federated Learning enables decentralized training of machine learning models by collaborating with multiple clients without sharing their data. However, limited labeled data in clients' domains can lead to overfitted local models and compromise the performance of the global model. To overcome this, we propose a novel approach by adopting a single source and multiple targets, considering variations in the style and movements of each client as distinct domains within Human Activity Recognition (HAR). The Modified Topology Preserving Domain Adaptation (modTPDA) is utilized to align the topological structure and preserve the underlying data manifold. Integrating modTPDA with the FL aims to adapt public source data in the server to various target domains with private data, accounting for their data topology. The proposed approach effectively addresses the issue of limited labeled data in Federated Learning for Human Activity Recognition.
Federated Learning-Based Credit Card Fraud Detection: Performance Analysis with Sampling Methods and Deep Learning Algorithms

The exponential technological advancement is turning everyone towards an easy and efficient way of financial transactions. Consequently, the use of credit cards is rising substantively, creating a more incredible opportunity for fraudsters which is an alarming concern nowadays since a fraudster may use several tools, techniques and tactics to make a fraudulent transaction. As a countermeasure, an effective fraud detection mechanism and highly sensitive data privacy preservation are imperative to detect fraudulent transactions. This paper proposes a Federated Learning (FL)-based fraud detection system since its key feature preserves the privacy of highly sensitive data, wherein the model could be trained without sharing the credit card data in the cloud. We contemplate three Deep Learning (DL) models: Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM) regarding the FL approach. Subsequently, to overcome the data imbalance issue, four distinct sampling techniques are explored to inspect the impact on the traditional centralized and FL approaches. Finally, we further investigate and compare FL-based detection systems with diversified state-of-the-art models. Our experimental results demonstrate that the proposed method is superior compared with state-of-the-art methods and achieves high detection rate of 99.51%, 98.77% and 98.20% respectively for CNN, MLP and LSTM models.
Efficient Privacy-Preserving Multi-Functional Data Aggregation Scheme for Multi-Tier IoT System

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme.
An Explainable Artificial Intelligence Approach for a Trustworthy Spam Detection

For decades, spam emails have been one of the most serious and irritating cybersecurity threats. For detecting spam emails, a variety of machine learning (ML) and deep learning (DL) approaches are used. These approaches identify spam emails in the inbox and send them to a junk folder. However, these approaches have some limitations, such as their inability to explain why an email is considered spam. The current paper introduces the X_SPAM approach by combining the machine learning technique (Random Forest) and deep learning technique (LSTM) to detect spam and uses the Explainable Artificial Intelligence technique (LIME) to increase the trustworthiness of spam detection by explaining the reason for their classification. We evaluate the proposed approach using two different datasets (LING without metadata and Enron with metadata). We found that the proposed approach has achieved a high accuracy rate for RF and LSTM at 98.13% and 99.13% respectively. Moreover, the study exhibits a visualizing manner to eliminate the black box drawback for ML and DL classifiers to increase the approach's trustworthiness.
Detection of Credit Card Fraud Detection Using HPO with Inception Based Deep Learning Model

The use of credit for together online and in-person acquisitions has skyrocketed in recent years because to developments in electronic commerce and communication networks. Credit card fraud, meanwhile, has been on the rise and is a major source of annual losses for banks and other financial institutions. To reduce these costs, accurate fraud detection algorithms are essential, but their development is complicated by the fact that most credit card databases are highly unbalanced. Because of their static of input vectors to output vectors, traditional machine learning procedures are inefficient when used to identify credit card fraud. As a result, they are unable to accommodate the ever-changing purchasing habits of credit card holders. With the advent of deep learning technology, it is now possible to automatically and correctly detect credit card fraud. This research study systematically enhanced the Inception-ResNet-v2 method and used it on real-time data. Using a convolutional neural network, this study presents a detection model that operates from beginning to end. This model incorporates the best features of Inception convolution and residual networks to solve the deep network's training difficulty and increase the network's breadth. Hunter-prey optimization (HPO) model is also used to select the best characteristics, further improving classification precision. Each model incorporates stratified K-fold cross-validation and a real-world dataset for detecting credit card fraud from European cardholders. As such, the suggested model is evaluated next to its contemporaries. According to the findings, the proposed model has a higher accuracy than its predecessors by a significant margin (96.57%).
Raindrop Removal using Image Inpainting

Image Restoration under severe weather circumstances has drawn a lot of interest for many computer vision applications. In order to deliver accurate and high quality surveillance in the context of smart cities, image de-raining is a crucial subject that has been explored extensively in recent years. In order to handle the challenge of removing raindrops, two different strategies are adopted in this research study: The Diffusion model and the Generative adversarial network model. By considering the recent improvements in image deraining methods, this research study proposes a novel technique that makes use of conditional generative adversarial network with adversarial loss, which provides a factor to loss functions and regulates the output for achieving the improved results. In addition, diffusion modelling, a novel patch-based method is used to perform image restoration. Diffusion probabilistic frameworks are used for normalising noise over affected regions. This research study compares and evaluates how well these two techniques perform in eliminating the raindrops from images. This study demonstrates that the diffusion model outperforms the GAN technique in terms of qualitative assessments and visual appearance by conducting a comparative analysis on actual and synthetic data.