Reviewerly/exHarmony huggingface dataset:
Advancing Security and Efficiency in Federated Learning Service Aggregation for Wireless Networks

Federated Learning (FL) is a distributed machine learning technique where multiple devices can collaboratively train a model without sharing their data. As a result, FL ensures distinct privacy benefits compared to centralized training approaches. However, despite its benefits, FL remains susceptible to reverse-engineering attacks that can uncover sensitive information about the training data from the local updates sent by each participant. To address this issue, we propose a framework for securely and efficiently aggregating the results of FL on multiple devices. We compare and evaluate the performance of two techniques, Homomorphic Encryption (HE) and Secure Multiparty Computation (SMPC), to determine the best method that allows devices to share their learning results without revealing their raw local data. Ultimately, we propose using SMPC protocol as the most effective solution to secure FL. Our framework is experimentally evaluated, and its effectiveness in terms of security, efficiency, and accuracy is demonstrated.
The Delineation of Copper Geochemical Blocks and the Identification of Ore-Related Anomalies Using Singularity Analysis of Stream Sediment Geochemical Data in the Middle and Lower Reaches of the Yangtze River and Its Adjacent Areas, China

The middle and lower reaches of the Yangtze River and its adjacent areas contain abundant mineral resources, especially porphyry–skarn–stratabound Cu–Au–Mo–Fe deposits, and still have great potential for mineral prospecting. In this paper, geochemical blocks and local singularity mapping methods were used to delineate the spatial distribution pattern of Cu and identify the geochemical anomalies related to Cu deposits. Six copper geochemical blocks, each with an area of more than 1000 km2, were all spatially consistent with the locations of the five Cu ore districts (Edongnan, Jiurui, Anqing-Guichi, Tongling, and Ningzhen) and one ore field (Dexing) in the study area. Thus, geochemical blocks delineated with low-density geochemical data can effectively track the locations of ore districts or large ore deposits. Most of the known Cu deposits in the study area were located in anomalous areas with singularity indices less than 1.741 in the Cu singularity map. The singularity analysis could reduce the anomalous areas and identify the geochemical anomalies related to Cu deposits effectively. Geochemical blocks combining a local singularity mapping method is an effective tool for identifying prospecting targets.
An Improved Method for Swing State Estimation in Multirotor Slung Load Applications

A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario.
Improving Decision Tree Accuracy through AdaBoost Ensemble with SMOTE Oversampling and ExtraTreeClassifier Feature Selection

The issue of detecting credit card fraud is prevalent in today's society due to the increasing reliance on the internet and the rise of online transactions and e-commerce platforms. Fraudulent activities occur when cards are stolen or when unauthorized individuals exploit credit card information. To address this problem, a credit card fraud detection system with high accuracy is developed using the Decision Tree algorithm. The main focus of this study is to address data imbalance and identify the most influential features using the ExtraTreeClassifier algorithm. Additionally, a combination of Decision Tree and AdaBoost is employed to enhance the accuracy of fraud detection. The effectiveness of these algorithms is measured through the use of metrics such as accuracy, precision, recall, and F1-score. The algorithms' effectiveness is further assessed through the examination of ROC AUC curves. Through the process of comparing the performance of the Decision Tree and the combined approach with AdaBoost, we can determine the most effective method for detecting fraudulent activities based on the algorithm that demonstrates the highest accuracy, precision, recall, and F1-score. This comparison allows us to identify the optimal model for effectively identifying and preventing fraudulent activities in various domains, providing valuable insights to mitigate potential threats.
Temporal-geographical attention-based transformer for point-of-interest recommendation

Point-of-Interest (POI) recommendation is one of the most important tasks in the field of social network analysis. Many efforts have been proposed to enhance the model performance for the POI recommendation task in recent years. Existing studies have revealed that the temporal factor and geographical factor are two crucial contextual factors which influence user decisions. However, they only learn representations of POIs and users from the single contextual factor and fuse the learned representations in the final stage, which ignores the interactions of different contextual factors, leading to learning suboptimal representations of POIs and users. To overcome this gap, we propose a novel Temporal-Geographical Attention-based Transformer (TGAT) for the POI recommendation task. Specifically, TGAT develops a hybrid sequence sampling strategy that samples the sequence of POIs from the different contextual factor POI graphs generated by the users’ check-in records. In this way, the interactions of different contextual factors can be care-fully pre-served. Then TGAT conducts a Transformer-based neural network backbone to learn representations of POIs from the sampling sequences. In addition, a weighted aggregation strategy is proposed to fuse the representations learned from different context factors. The extensive experimental results on real-world datasets have demonstrated the effectiveness of TGAT.
Relative Synergy Coefficient: A novel way to detect variable interaction in large dataset

Feature interaction, also referred to as feature synergy, denotes the phenomenon wherein interactive features collectively convey more information than their individual contributions, thereby holding paramount significance in the realms of feature engineering and data mining. Many prevailing techniques designed to detect these interactions primarily rely on model-based methods to compute absolute synergy. However, such approaches often prove ill-suited for extensive datasets and overlook variables with comparatively minor primary effects. In response, we introduce a groundbreaking metric known as the Relative Synergy Coefficient (RSC). This novel metric facilitates swift identification and quantification of relative synergy’s potency within large datasets. The proposed indicator is a non-parametric metric based on information entropy, and its computation involves the utilization of discretization and normalization techniques. The generality, equitability and robustness of metric is proved on the simulated data. Besides, the indicator is proved to be effective and can cross-validate with domain knowledge on two real world datasets.
Train vs. Play: Evaluating the Effects of Gamified and Non-Gamified Wheelchair Skills Training Using Virtual Reality

This study compares the influence of a gamified and a non-gamified virtual reality (VR) environment on wheelchair skills training. In specific, the study explores the integration of gamification elements and their influence on wheelchair driving performance in VR-based training. Twenty-two non-disabled participants volunteered for the study, of whom eleven undertook the gamified VR training, and eleven engaged in the non-gamified VR training. To measure the efficacy of the VR-based wheelchair skills training, we captured the heart rate (HR), number of joystick movements, completion time, and number of collisions. In addition, an adapted version of the Wheelchair Skills Training Program Questionnaire (WSTP-Q), the Igroup Presence Questionnaire (IPQ), and the Simulator Sickness Questionnaire (SSQ) questionnaires were administered after the VR training. The results showed no differences in wheelchair driving performance, the level of involvement, or the ratings of presence between the two environments. In contrast, the perceived cybersickness was statistically higher for the group of participants who trained in the non-gamified VR environment. Remarkably, heightened cybersickness symptoms aligned with increased HR, suggesting physiological connections. As such, while direct gamification effects on the efficacy of VR-based wheelchair skills training were not statistically significant, its potential to amplify user engagement and reduce cybersickness is evident.
Development and Implementation of Voice-Controlled 3D Movement of Robotic Arm Based on Embedded System

In this paper, we propose a method for a robotic arm based on human–machine interface, which can assist physically disabled people to perform activities of their day-to-day life. The interface can be implemented in several ways, viz. by using voice command or hand gesture or through electrooculography. In the propose system, human voice command is used to control the prototype devices. An HM2007 IC is used for the voice command processing and recognition. The analog input voice command given by the user is converted to the digital output signal by the speech recognition IC. The microcontroller further processes these digital outputs for motion and directional control of the robotic arm. The paper includes the results obtained by testing the system for both speaker dependent and speaker independent cases, respectively, under noise-free and noisy environment.
An Integrated Photoelasticity-Based Approach for the Reconstruction of Stress Profiles and Optical Anisotropy of GRIN Lenses

The paper shows that it is possible to obtain reliable information on the dependence of the radial distribution of longitudinal birefringence in glass cylindrical elements with a radial distribution of refractive index (GRIN lenses) on the basis of transverse polarization tomography data on residual stresses. This does not require complicated procedures of sample preparation, as is necessary in the case of longitudinal translucency measurements. The approach developed was verified in the experiments with a set of different GRIN lenses formed with the ion exchange technique, and the closeness of the data obtained from transversal and conventional longitudinal transmission measurements was demonstrated.
The Rise of Hybrid Educators

Highlights We live in a technology-driven age, so the education field must rationally consider chatbots powered by artificial intelligence (AI). Faced with increasing application of AI in education, teachers should be reflective educators, and students should be self-educators. AI chatbots will evolve into a new prosthesis before being institutionalized into the fabric of school education. Reflective, institutionalized, self-educators foreshadow the future rise of hybrid educators.
A novel multi-layer feature fusion-based BERT-CNN for sentence representation learning and classification

Purpose The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis. Design/methodology/approach The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model. Findings On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets. Originality/value The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.
Malaria Detection Using Convolutional Neural Networks: A Comparative Study

The potentially fatal parasitic disease known as malaria is spread to people by mosquito bites. For the disease to be effectively treated and controlled, early malaria detection is essential. Convolutional neural networks (CNNs), a type of deep learning algorithm, have recently demonstrated considerable potential in the detection of malaria from blood smear images. In this study, we assess how well four CNN models-GoogLeNet, DenseNet161, MobileNet_v2, and ResNet18-performed at identifying malaria from a dataset that was made available to the public. Our results show that DenseNet161 achieved the highest accuracy on the test set (95.86%), followed by ResNet18 (95.08%), GoogLeNet (94.85%), and MobileNet_ v2 (94.88%). These results imply that deep learning algorithms are capable of detecting malaria and that selecting the right model is essential for achieving high accuracy.
Att-KGCN: Tourist Attractions Recommendation System by Using Attention Mechanism and Knowledge Graph Convolution Network

The recommendation algorithm based on knowledge graphs and deep learning is at a relatively mature stage. However, there are still some problems in the recommendation of specific areas. For example, in the tourism field, selecting suitable tourist attraction attributes process is complicated as the recommendation basis for tourist attractions. In this paper, Based on Knowledge Graph Convolution Network as a deep learning model, we propose the improved Attention Knowledge Graph Convolution Network model, named (Att) - KGCN), which automatically discovers the neighboring entities of the target scenic spot semantically. The attention layer aggregates relatively similar locations and represents them with an adjacent vector. Then, according to the tourist's preferred choices, the model predicts the probability of similar spots as a recommen-dation system. A knowledge graph dataset of tourist attractions used based on tourism data on Socotra Island-Yemen. Through experiments, it is verified that the Attention Knowledge Graph Convolution Network has a good effect on the recommendation of tourist attractions and can make more recommendations for tourists' choices.
Bird Detection on Power Transmission Lines Based on Improved YOLOv7

The safety of transmission lines is essential for ensuring the secure and dependable operation of the power grid. However, the harm caused by birds to transmission lines poses a direct threat to their safe operation. The main challenges in detecting birds on lines is that the detected targets are small, densely packed, and susceptible to environmental interference. We introduce a novel dynamic convolutional kernel specifically designed for detecting small and densely packed targets, the ODconv in the backbone of YOLOv7, to capture richer contextual information and improve performance. The substitution of Alpha_GIoU for CIoU in the original YOLOv7 network model serves to refine the loss function, decrease its parameters, and bolster the network’s resilience. The results confirmed that the proposed YOLOv7 with ODConv reached mAP0.5, mAP0.5:0.95, and precision of up to 78.42%, 46.14%, and 73.56% respectively. In contrast to the base model, the enhanced model demonstrated a 2.58% rise in mAP0.5, a 0.72% improvement in mAP0.5:0.95, and an increased precision of 2.34%.
Local climate variability associated to sustainable agriculture at rain shadow areas of mount Rinjani and Tambora in West Nusa Tanggara

Abstract Climate is driven by solar energy received on the earth surface and local factor, such as mountains. The solar energy affects climate variables namely, temperature, atmospheric pressure, relative humidity, wind and rain. In addition, mountains perform rain shadow areas and affect variability of local climate. A descriptive research was conducted to study variability of those variables associated to direct normal solar irradiation (DNI), and its possible impact on sustainable agriculture at rain shadow areas of Mt. Rinjani in Lombok and Tambora in Sumbawa, West Nusa Tenggara. The results show that the DNI was relative similar at both sides of the Mt. Rinjani which was 13.15±2.6 MJ m −2 day −1 on average. While at Mt. Tambora, the DNI at rain shadow area (southern part) was higher than northern part which were 15.6±2.07 MJ.m −2 .day −1 and 13.6±3.2 MJ m −2 .day −1 respectively. The DNI was relatively high in low lands and it successively decreased through medium and high lands. ETo, air and soil surface temperature were relatively high at rain shadow areas. In conclusion, the intensity of DNI was not significantly affected by aspect factor of incoming light relative to the Mt Rinjani and Mt. Tambora. The mountains had affected the variability of local climate and performed rain shadow areas to the north from Mt Rinjani and to the south from Mt. Tambora, where there had established typical dry land agriculture of semi arid tropics.
Multi-Hop Reasoning of Temporal Knowledge Graph Based on Reinforcement Learning

The reasoning research of temporal knowledge graph has attracted many researchers' attention in recent years. Among the existing reasoning methods, scholars mainly focus on the research of multi-hop reasoning tasks under future timestamps. Multi-hop reasoning refers to making multiple jumps in the temporal knowledge graph to reason new information by using the relation between entities and timestamp. In the multi-hop reasoning task, it is necessary to consider the semantics of facts, which is often reflected in the semantic relation between entities or between entities and relations. Additionally, with the continuous evolution of timestamps, the historical state under different timestamps will have different effects on the reasoning results. Therefore, the current research on multi-hop reasoning of temporal knowledge graph faces two challenges: How to model the entity semantic hierarchy in temporal knowledge graphs. How to enhance the modeling ability of temporal sequence of historical states, so that we can obtain more accurate temporal evolutional information between entities. For the sake of addressing the above-mentioned two challenges, we put forward a neoteric reinforcement learning reasoning model. Specifically, firstly, we embed entities, relations and timestamps into polar coordinates by means of representation learning, and introduce the obtained entity embedding vectors into the knowledge graph reasoning model based on reinforcement learning, which can provide coded representations of the entities with hierarchical relations and help to provide more semantic information between facts. Then, we introduce a component called temporal information integration module based on self-attention network. It contribute to comprehend and employ temporal evolutionary patterns for more accurate reasoning. Experimental results prove that our proposed model performs better than other state-of-the-art methods on four public datasets.
Generalized Quantum Convolution for Multidimensional Data

The convolution operation plays a vital role in a wide range of critical algorithms across various domains, such as digital image processing, convolutional neural networks, and quantum machine learning. In existing implementations, particularly in quantum neural networks, convolution operations are usually approximated by the application of filters with data strides that are equal to the filter window sizes. One challenge with these implementations is preserving the spatial and temporal localities of the input features, specifically for data with higher dimensions. In addition, the deep circuits required to perform quantum convolution with a unity stride, especially for multidimensional data, increase the risk of violating decoherence constraints. In this work, we propose depth-optimized circuits for performing generalized multidimensional quantum convolution operations with unity stride targeting applications that process data with high dimensions, such as hyperspectral imagery and remote sensing. We experimentally evaluate and demonstrate the applicability of the proposed techniques by using real-world, high-resolution, multidimensional image data on a state-of-the-art quantum simulator from IBM Quantum.
Pre-Processing Of Resize And Region Of Interest (ROI) To Improve The Accuracy Of Batik Detection Based On VGG-16

The patterns and designs used in batik are a traditional textile art form from Indonesia. With the increasing popularity of computer vision applications, the interest in automatic detection and recognition of batik patterns is increasing. Convolutional Neural Networks (CNNs) show very good performance in detecting objects. The problem addressed in this study is the variation in image sizes, which affects accuracy. The selection of pre-processing techniques will impact the accuracy of the results. For this reason, it is important in this study to compare tests on data that has been processed using resize and Region of Interest (ROI). This study aims to determine the impact of two pre-processing techniques, resize and ROI, on the accuracy of batik pattern recognition using the Visual Geometry Group (VGG)-16 model. The dataset consisted of 1,445 images, with 1,301 images used for training and 144 images used for testing. The classes used were Kawung, Parang, Satriomanah, Sawat, Sementrante, Sidomukti, Tambal, and Truntum. The experimental results demonstrate that the choice of pre-processing techniques significantly affects the accuracy of batik pattern detection. Resizing provides an efficient computational solution, while ROI achieves a detection accuracy of 0.96, which is superior to Resize's accuracy of 0.89. This study highlights the importance of preprocessing techniques in detecting batik patterns using the VGG-16 model. Resizing and ROI have advantages and disadvantages, ROI techniques generally result in higher accuracy.