Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of a Tourist Attraction Recommendation Model Integrating Differential Privacy

A tourist attraction recommendation model that integrates differential privacy is proposed to address the issue of user privacy leakage in current smart tourism recommendation systems. This model utilizes convolutional neural networks to generate feature vectors for users' historical or browsing records of scenic spots. Then, based on the different levels of user interest, different differential privacy noise is added to the feature vectors of each attraction of the user, and the total potential feature vectors of the user are calculated using a weighted sum method. To ensure the security of user data, a unified Laplacian noise is added to the potential feature vector of the user, and similarity calculation is performed with the feature vector of the candidate scenic spot to be recommended. When the similarity exceeds a certain threshold, the scenic spot is recommended to the user.
Design of a Virtual Environment with Haptic Interface for Enhanced Immersive Experiences

This paper describes the design of a virtual environment with haptic feedback developed as a multisensory experience. This was designed as a tactile game that integrates a classical videogame, but in which, objects with predefined tactile properties can be recognizable by users thanks to a special screen that provides tactile feedback. The development of the experience entailed considering design parameters such as the user's adaptability to the environment, textures, and playability. The degree of immersion in the experience was evaluated by designing scenarios with sweeps of predefined variables. Users reported great acceptance of the characteristics and use of the virtual environment by being able to interact more directly with the elements that make it up.
Modeling User Profile in Gamification Based e-Learning Using k-Means Clustering

The current development of e-Learning is still in the form of simple e-Learning as a place to share materials and do the exercises given by lecturers. This should be a problem since such e-Learning activities are not different from conventional learning and do not facilitate a personalized learning process. Personalized learning is important to address students’ different comprehension abilities in the learning process. Personalization can be done by characterizing student profiles. Therefore, we need a system to determine profiles by modeling user profiles in e-Learning. In this study, personalization was done by recognizing the users’ characteristics obtained from internal factors including personality, achievement emotion, and student activity logs when engaging in the e-Learning process. Student activity logs were automatically recorded by the system. To find out the profile of existing user, grouping was done using the unsupervised machine learning method, namely k-Means Clustering. Two k-clusters were employed to determine the user profiles. Two grouped profiles indicated differences in characteristics of the user profile group. The results of the cluster evaluation show the value of Silhouette Score of 0.43 and Connectivity Score of 8.85.
Reducing Label Errors for Actionable Warning Identification

Machine Learning-based Actionable Warning Identification (ML-based AWI) has attracted a great deal of research work in recent years. A reliable warning dataset is critical to evaluate the effectiveness of ML-based AWI approaches. Unfortunately, the warning dataset used in the ML-based AWI community are automatically labeled by the closed-based warning heuristic, which is prone to mislabeling. Such mislabeled warning datasets could lead to the inaccurate evaluation of ML-based AWI approaches. Removing the label noises from the warning dataset is a very challenging task. To address this problem, we propose an effective approach to reduce label errors in AWI. Specifically, we creatively introduce Confident Learning (CL), a state-of-the-art noise estimation technique, to the ML-based AWI community. To mitigate false positives introduced by CL, we incorporate the idea of ensemble learning to achieve a more robust capability in noise detection. Based on a reliable warning dataset from 10 real-world and large-scale projects, the experimental results show that our approach can be effective in detecting label noises.
Validation algorithm for aligning postal addresses available on the Internet*

The internet often serves as a vast pool of information that can be utilized to retrieve personal information and locate individuals. Typically, governmental institutions in higher education provide addresses to people associated with them. However, the information available on the internet relating to personal data is not always accurate and can lead to misunderstandings, especially concerning the urgency of finding the right person at the right time.One potential solution is the development of an algorithm for validating and verifying addresses. This algorithm could help identify outdated or incorrect address information within personal data available on the internet. This issue could be addressed by implementing an algorithm that incorporates a verification mechanism for addresses, ensuring users receive accurate information.In this paper, it is proposed a novel algorithm for address verification utilizing ROBERTA. The paper outlines an address verification algorithm that leverages the Hugging Face Transformers Library to implement the ROBERTA model proposed in the study. This algorithm is applied to the personal address data available on the internet. The algorithm’s performance is evaluated, yielding notably positive results in address validation.
Fed-STP: An Improved Federated Learning Approach via Stage-wise Training and Pruning

Federated Learning (FL) is a transformative machine learning paradigm, allowing multiple clients to train a global model while maintaining local data privacy collaboratively. However, each client's unique local data distribution introduces challenges, leading to imbalances in individual model optimization and affecting model performance and convergence in non-Independent and Identically Distributed (non-IID) settings. To mitigate these issues, we propose an innovative federated learning algorithm, 'Federated Learning with Stage-wise Training and Pruning' (Fed-STP), aiming to optimize model performance under non-IID conditions. Fed-STP employs client-specific model pruning and a stage-wise training strategy to enhance local models' adaptability to their unique data characteristics. This approach also minimizes the information loss resulting from model parameter variations during the server aggregation process, thus indirectly enhancing the overall performance of the federated global model. Comprehensive experiments and detailed analyses confirm Fed-STP's superior performance over conventional FL methods, showing significant improvements in diverse image classification tasks, mainly when dealing with non-IID client data, with performance enhancements reaching up to 9.25%.
Hyperspectral Image Denoising: A Comparative Study On Uav Based Vegetation Data

Hyperspectral imaging (HSI) sensors can identify distinctive features of objects by providing rich spectral information in hundreds of narrow wavebands. Unmanned aerial vehicle (UAV) based HSI technology has become popular in remote sensing (RS) applications due to extensive coverages, short revisiting times, and high spectral and spatial resolutions. However, in hyperspectral (HS) RS with UAVs, the quality of the images degrades due to the acquisition process, noise, and platform instability, which affect the end-user applications. HSI denoising is an essential pre-processing operation that enhances the data quality. This paper compared the suitability and efficacy of state-of-the-art denoising algorithms for crop and vegetation HSI data captured from a UAV platform. We observed that FastHyMix and FastHyDe are well-performing and time-efficient denoising methods for UAV-based HS images. Also, we studied the effect of denoising on HSI classification using a UAV-based vegetation dataset. This study will be helpful in UAV-based RS applications in enhancing HSI data quality during the pre-processing stage.
Direct Position Determination Based on Maximum Likelihood under Non-uniform Noise

The direct position determination (DPD) algorithm based on maximum likelihood iteration is proposed under non-uniform noise. In this paper, the negative log-likelihood function of signal and noise covariance matrix is introduced, and the covariance matrix of array is reconstructed by iteratively solving the non-uniform noise covariance matrix, and the robust direct location is realized by subspace principle. Simulation results show that the proposed algorithm can accurately determine the target position with fewer iterations.
Control of Distributed Generation Systems , Part I : Voltages and Currents Control

This paper discusses a digital control strategy for three-phase PWM voltage inverters used in a single stand-alone AC Distributed Generation System (DGS). The proposed control strategy utilizes the perfect robust servomechanism problem control theory to allow elimination of specified unwanted voltage harmonics from the output voltages under severe non-linear load and to achieve fast recovery performance on load transient. This technique is combined with a discrete sliding mode current controller that provides fast current limiting capability necessary under overload or short circuit conditions. The proposed control strategy has been implemented on a DSP (Digital Signal Processor) system and experimentally tested on an 80 kVA prototype unit. The results showed the effectiveness of the proposed control algorithm Index terms – three-phase PWM inverters, digital signal processor (DSP), robust servomechanism control, and sliding mode control. I.
Automatic Image Registration using SIFT and AKAZE: An application for Preservation of Cultural Heritage

Automatic image registration, which aligns several images acquired from different viewpoints or timeframes, plays an important role in the analysis and preservation of cultural heritage. It serves as a crucial preprocessing step for performing comparative analysis, restoration, and reconstruction of cultural heritage images. In this paper, an automatic image registration approach is proposed that combines the strengths of both the Scale Invariant Feature Transform (SIFT) and the Accelerated KAZE (AKAZE) for accurate and robust registration of heritage images. While SIFT excels in matching images with varying scales and rotations, AKAZE is renowned for its robustness to non-linear transformations and challenging imaging conditions. This fusion of SIFT and AKAZE feature detection leads to a substantial increase in accurate correspondences, enhancing registration results. The effectiveness of this proposed approach has been assessed using the Affine Covariant Regions Dataset and the Indian Heritage Image Retrieval Dataset (IHIRD). The outcomes of the experiments show that the proposed method increases the number of correct correspondences when compared to those of SIFT or AKAZE alone.
A Deep Double-Convolutional Neural Network-Based Fault Detection

Fault detection is crucial for saving valuable maintenance time and costs of industrial systems when faults occur. Faults are usually cataloged into sudden faults and degraded faults, which have diverse impacts on system safety. So, distinct treatment should be treated during fault detection. In this paper, an intelligent fault detection method is proposed based on a double-convolutional neural network (CNN) model architecture to detect whether faults occur and which type they belong. Firstly, a CNN model is employed to judge whether faults occur. Furthermore, the "Majority rule" is applied to effectively eliminate the influence of outliers for enhancing the robustness of fault detection model. Then, Another CNN model combined with root-mean-square (RMS) is introduced to detect fault types that belong to the sudden fault or degraded fault. Finally, an experimental study involving four types of bearing degradation scenarios is conducted to validate the effectiveness of the proposed method.
High Gain Metamaterial Superstrate Loaded Antenna For S band Communication

A low profile microstrip patch antenna loaded with dumbbell shaped slots is stacked with a metamaterial in S band. The antenna and the metamaterial superstrate with separation of 5.5mm forms a Fabry-Perot-Cavity-like leaky wave antenna. The efficient metamaterial geometry causes phase delays in surface waves travelling over it and hence helps in storing and reradiating energy. The antenna with a dimension of 25×25 mm2 achieves measured band operation from 3.2-3.6GHz giving a peak gain of 6.25dBi at resonant frequency of 3.3GHz. Improved efficiency of 80.63% and gain enhancement of 4.5dBi confirms the geometry suitable for satellite communications and radars.
Quality Control Based on Species Classification of Herbal Medicines Using Hyperspectral Imaging and Machine Learning

The utilization of herbal medicines for primary healthcare needs is prevalent among the majority of the global population, estimated to be around 80%. There is a mandatory requirement to establish quality assurance parameters for individual raw drugs and combination formulations: the proper classification of the herbs/plants to be used. This work proposes a new methodology for quality control in terms of classification of plants from the Brazilian cerrado. Hyperspectral images of powdered leaves of plants of the Acanthaceae family were obtained. Two species identification is done using principal components analysis (PCA), partial least squares and discriminant analysis (PLS-DA). Qualitatively, the results obtained from the PCA showed the separation of the clusters, highlighting the spectral differences between the classes. The classification obtained using PLS-DA identified the spectra between J. pectoralis and J. thunbergioides above 95% for all samples tested.
Towards DGA Domain Name Detection via Multi-feature Coordinated Representation and Random Forest

As a common attack strategy in botnet, malicious domain name tends to cause huge property loss and serious privacy disclosure. Domain generation algorithm (DGA) is commonly used to produce a huge of random Fast-Flux domain names to release malicious information by controlling bots. As a classic strategy, blacklisting-based approach has been widely used in the field of DGA domain name detection. However, DGA blacklist is not easy to obtain and the reverse analysis of DGA seeds is quite challenging. In this work, an innovative DGA detection method based on coordinated representation of multimodal features and Random Forest (MCRF) is proposed to achieve high-performance DGA domain name detection. For the considered datasets, the whitelist is composed of 350,000 normal domains from Alexa Top 1 million and 350,000 normal domains from Tranco. The blacklist is established by crawling 350,000 DGA domains from 360 NetLab. Next, a total of eight dimensional features of the three modalities are aggregated through sufficient statistical analysis to form the coordinated representation for domain names. Finally, Random Forest algorithm is employed to implement binary classification in terms of normal domains and DGA domains. The comparative experiments illustrate that MCRF sets a new baseline for DGA domain name detection.
An Extensive Investigation on Blockchain integrated Tumor Segmentation Approaches on Magnetic Resonance Brain Images

Segmenting a brain tumour entail dividing the various tumour tissues from healthy brain tissues. Image segmentation is incredibly important for the efficient extraction of properties as well as details from many kinds of multimodal images. Extraction of patient-specific critical clinical data and related diagnostic features is the principal objective of brain tumour imaging evaluation. Once an infection has been identified and localised, the data included in the multidimensional images can direct and track therapies, eventually resulting in expertise for the clinical assessment, illness stage, and intervention. One of the important concerns in the medical field is to secure the patient's personal and health information. In order to overcome this, a blockchain (BC) based healthcare system is adopted that enables safe, unintrusive, and rapid data transfer. Numerous experts in the fields of medical imaging and soft computing have previously conducted extensive research on the segmentation of brain tumours using both semi and fully automated techniques. In-depth literature reviews of current techniques for tumour segmentation and blockchain technology used for encrypted medical information transfer are addressed in this article. Modern techniques are used, and their performance and quantitative analysis are included. With the most current contributions from numerous researchers, various image segmentation techniques are briefly discussed. In this instance, an attempt is made to provide readers with fresh perspectives on the subject of the research.
New constraints on the neotectonic stress pattern of the Flinders and Mount Lofty Ranges, South Australia

The majority of published in-situ stress information in the Australian continent is confined to petroleum provinces where industry technologies facilitate the capture of contemporary crustal stress information. In contrast, the stress pattern of non-petroleum regions such as the Flinders and Mount Lofty Ranges in South Australia, where intraplate deformation is localised, has not been investigated comprehensively so far. The ongoing activities of the mining industry in South Australia has enabled us to access recently drilled boreholes for image logging techniques that have typically been under-utilised by the mineral sector. Herein, we conduct stress analysis by analysing borehole image logs from 16 boreholes in the basement rocks of South Australia as well as two geothermal wells and one coal seam gas well in South Australia’s northern Flinders Ranges, the Gawler Craton and the Eyre Peninsula. The resulting dataset of stress orientations is further accomplished by including recent seismological observations, which provide crustal stress information derived from focal mechanism solutions. The results of this study suggest a regional E–W orientation of the maximum horizontal compressive stress that is consistent with numerous observed neotectonic structures in this region. The focal mechanism solutions in this study suggest that the majority of events occur in a thrust faulting stress regime, which is consistent with the observed Quaternary fault scarps. However, our data compilation also indicates the presence of strike-slip and normal faulting stress regimes in the region, which has not been suggested extensively before this study. The results of this study suggest a regional E–W orientation of the maximum horizontal stress in the Flinders and Mount Lofty Ranges. Focal mechanism solutions of earthquakes suggest that the majority of events occur in a thrust faulting stress regime. However, our data also indicate the presence of strike-slip and normal faulting stress regimes in the region, which has not been suggested extensively before this study.
A Pilot Study to Assess the Effects of Guided Voice-Synthesized Mindfulness Meditation on University Students through EEG Measurement

Mindfulness meditation has shown promising results for students; however, meditations generated through text-to-speech (TTS) are not as prevalent. Determining whether technologically developed Mindfulness meditations can yield positive outcomes is vital. This article presents the results of a pilot study on the effects of TTS-generated guided meditation on university students. Twelve participants engaged in a six-minute session of reading and meditation while their brainwave data was recorded. The study measured six psychological and physiological states. Four variables, Stress, Excitement, Focus, and Interest showed significant improvements after the Mindfulness Meditation, which suggests potential benefits for students. However, according to the Wilcoxon signed-rank test, two variables showed no significant effects. The study recommends further research with larger samples and longer durations to understand better the potential benefits of TTS-generated guided meditation in educational settings.
A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches

In the context of high-dimensional credit card fraud data, feature selection techniques are commonly employed by researchers and practitioners to enhance the performance of credit card fraud detection models. This study presents a comparison in model performance using the most important features selected by SHAP (SHapley Additive exPlanations) values and the model's built-in feature importance list. Both methods rank features and select the most important features for model evaluation. The performance of these feature selection techniques is assessed by building classification models using two classifiers: XGBoost and Decision Tree. The evaluation metric used to measure effectiveness is the Area under the Precision-Recall Curve (AUPRC). All experiments are conducted on the Credit Card Fraud Detection Dataset from Kaggle. The experimental results and z-tests demon-strate that, in most cases, there is no significant difference between the two feature selection methods, regardless of the feature subset size and classifier used. However, for models trained on larger datasets, it is suggested to use the model's built-in feature importance list as the preferred feature selection method over SHAP. The rationale behind this recommendation is that computing SHAP feature importance is a separate activity, and models provide feature importance as a side-effect of training, thus requiring no extra effort. Therefore, opting for the model's built-in feature importance list can offer a more efficient and manageable approach for larger datasets and more complex models.
The Application in Texture Surface Inspection with Anomaly Detection Based on Smart Camera and Flexible Vibration Feeders System

This paper introduces a flexible vibration system combined with a machine vision system. The workpiece guide mechanism works powerfully and flexibly with different types of vibrating bowl workpieces. The system collects images of the workpiece with a smart camera and then identifies the features of the defect on the surface of the workpiece. We used unsupervised learning anomaly detection to detect defects. This method is embedded in our designed software called Machine Vision Camera. The defect properties of the product are compared with those of the normally moving workpiece at the outlet of the feed system and the conveyor. Defective workpieces are removed from the line by the system controller. The system of us improves the finished product yield of the vibrating bowl feeding system and reduces the cost and time of product inspection. This study has important implications for providing a solution for defective workpiece detection and removal with a reject system in a flexible manufacturing system that responds rapidly to the challenges of the industrial environment at present.