Reviewerly/exHarmony huggingface dataset:
Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.
SkyroadAR: An Augmented Reality System for UAVs Low-Altitude Public Air Route Visualization

Augmented Reality (AR) technology visualizes virtual objects in the real environment, offering users an immersive experience that enhances their spatial perception of virtual objects. This makes AR an important tool for visualization in engineering, education, and gaming. The Unmanned Aerial Vehicles’ (UAVs’) low-altitude public air route (Skyroad) is a forward-looking virtual transportation infrastructure flying over complex terrain, presenting challenges for user perception due to its invisibility. In order to achieve a 3D and intuitive visualization of Skyroad, this paper proposes an AR visualization framework based on a physical sandbox. The framework consists of four processes: reconstructing and 3D-printing a sandbox model, producing virtual scenes for UAVs Skyroad, implementing a markerless registration and tracking method, and displaying Skyroad scenes on the sandbox with GPU-based occlusion handling. With the support of the framework, a mobile application called SkyroadAR was developed. System performance tests and user questionnaires were conducted on SkyroadAR; the results showed that our approachs to tracking and occlusion provided an efficient and stable AR effect for Skyroad. This intuitive visualization is recognized by both professional and non-professional users.
Boosting SAR Aircraft Detection Performance with Multi-Stage Domain Adaptation Training

Deep learning has achieved significant success in various synthetic aperture radar (SAR) imagery interpretation tasks. However, automatic aircraft detection is still challenging due to the high labeling cost and limited data quantity. To address this issue, we propose a multi-stage domain adaptation training framework to efficiently transfer the knowledge from optical imagery and boost SAR aircraft detection performance. To overcome the significant domain discrepancy between optical and SAR images, the training process can be divided into three stages: image translation, domain adaptive pretraining, and domain adaptive finetuning. First, CycleGAN is used to translate optical images into SAR-style images and reduce global-level image divergence. Next, we propose multilayer feature alignment to further reduce the local-level feature distribution distance. By applying domain adversarial learning in both the pretrain and finetune stages, the detector can learn to extract domain-invariant features that are beneficial to the learning of generic aircraft characteristics. To evaluate the proposed method, extensive experiments were conducted on a self-built SAR aircraft detection dataset. The results indicate that by using the proposed training framework, the average precision of Faster RCNN gained an increase of 2.4, and that of YOLOv3 was improved by 2.6, which outperformed other domain adaptation methods. By reducing the domain discrepancy between optical and SAR in three progressive stages, the proposed method can effectively mitigate the domain shift, thereby enhancing the efficiency of knowledge transfer. It greatly improves the detection performance of aircraft and offers an effective approach to address the limited training data problem of SAR aircraft detection.
A Denoising Method for Multi-Noise on Steel Surface Detection

In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.
A swin transformer based bird nest detection approach with unmanned aerial vehicle images for power distribution and pole towers

Abstract The authors propose a novel object detection algorithm for identifying bird nests in medium voltage power line aerial images, which is crucial for ensuring the safe operation of the power grid. The algorithm utilises an improved Swin Transformer as the main feature extraction network of Fast R‐CNN, further enhanced with a channel attention and modified binary self‐attention mechanism to improve the feature representation ability. The proposed algorithm is evaluated on a newly constructed image dataset of medium voltage transmission lines containing bird nests, which are annotated and classified. Experimental results show that the proposed algorithm achieves satisfied accuracy and robustness in recognising bird nests compared to traditional algorithms.
Malicious Account Identification in Social Network Platforms

Today, people of all ages are increasingly using Web platforms for social interaction. Consequently, many tasks are being transferred over social networks, like advertisements, political communications, and so on, yielding vast volumes of data disseminated over the network. However, this raises several concerns regarding the truthfulness of such data and the accounts generating them. Malicious users often manipulate data to gain profit. For example, malicious users often create fake accounts and fake followers to increase their popularity and attract more sponsors, followers, and so on, potentially producing several negative implications that impact the whole society. To deal with these issues, it is necessary to increase the capability to properly identify fake accounts and followers. By exploiting automatically extracted data correlations characterizing meaningful patterns of malicious accounts, in this article we propose a new feature engineering strategy to augment the social network account dataset with additional features, aiming to enhance the capability of existing machine learning strategies to discriminate fake accounts. Experimental results produced through several machine learning models on account datasets of both the Twitter and the Instagram platforms highlight the effectiveness of the proposed approach toward the automatic discrimination of fake accounts. The choice of Twitter is mainly due to its strict privacy laws, and because its the only social network platform making data of their accounts publicly available.
Epidemiology of neurological disorders in Warangal, India – risk factor assessment-based prospective observational study

1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–1222. https://doi.org/10.1016/S0140-.... CrossRef Google Scholar
OIE4PA: open information extraction for the public administration

Abstract Tenders are powerful means of investment of public funds and represent a strategic development resource. Despite the efforts made so far by governments at national and international levels to digitalise documents related to the Public Administration sector, most of the information is still available in an unstructured format only. With the aim of bridging this gap, we present OIE4PA, our latest study on extracting and classifying relations from tenders of the Public Administration. Our work focuses on the Italian language, where the availability of linguistic resources to perform Natural Language Processing tasks is considerably limited. Nevertheless, OIE4PA adopts a multilingual approach so it can be applied to several languages by providing appropriate training data. Rather than purely training a classifier on a portion of the extracted relations, the backbone idea of our learning strategy is to put a supervised method based on self-training to the proof and to assess whether or not it improves the performance of the classifier. For evaluation purposes, we built a dataset composed of 2,000 triples which have been manually annotated by two human experts. The in-vitro evaluation shows that OIE4PA achieves a MacroF $$_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>1</mml:mn> </mml:msub> </mml:math> equal to 0.89 and a 91 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> accuracy. In addition, OIE4PA was used as the pillar of a prototype search engine, which has been evaluated through an in-vivo experiment with positive feedback from 32 final users, obtaining a SUS score equal to 83.98 .
Feature selection algorithm for substation main equipment defect text mining based on natural language processing

Abstract The dimension of relevant text feature space and feature weight of substation main equipment defect information is high, so it is difficult to accurately select mining features. The Natural Language Processing (NLP) medium and short‐term neural network model is used to realise the defect information text feature word segmentation in the log. After extracting the text features of defect information of main substation equipment with high categories to form the feature space; the TF‐IDF algorithm is designed to calculate the importance weight of text keywords, judge the criticality of defect information text feature vocabulary, accurately locate defect information text features, and realise defect information text feature mining. Experiments show that the algorithm has high precision for specific word segmentation of massive substation main equipment log information.
Genuinely noncyclic geometric gates in two-pulse schemes

While most approaches to geometric quantum computation are based on geometric phases in cyclic evolution, noncyclic geometric gates have been proposed to increase further the flexibility. While these gates remove the dynamical phase of the computational basis, they do not, in general, remove it from the eigenstates of the time evolution operator, which makes the geometric nature of the gates ambiguous. Here, we resolve this ambiguity by proposing a scheme for genuinely noncyclic geometric gates. These gates are obtained by evolving the computational basis along open paths consisting of geodesic segments, and simultaneously assuring that no dynamical phase is acquired by the eigenstates of the time evolution operator. While we illustrate the scheme for the simplest nontrivial case of two geodesic segments starting at each computational basis state of a single qubit, the scheme can be straightforwardly extended to more elaborate paths, more qubits, or even qudits.
Cybercrime and Harassment: The Impact of Blackmailing on Jordanian Society as a Case Study

This study aims to uncover the relationship between two cybercrimes, harassment and blackmailing, as well as their impact on Jordanian society. The study population included 90 prosecutors working in Jordanian courts. The researchers used the relational method in the second half of the academic year 2020. According to Jordanian prosecutors, the rate of cybercrime harassment is average, whereas that of blackmail is high. The findings also show a statistically significant relationship between harassment and blackmail crimes among the sample members, which is a statistically significant rate. The study also showed that the spread of harassment had six consequences: threatening and defaming the victim, family breakup, social decay, loss of values, instilling skcepticism and loss of self-confidence, and security instability. The authors recommend increasing citizens' awareness of the concept of electronic governance to combat cybercrime. They also recommend that governments conclude agreements and treaties that criminalize all types of crimes, pinpoint their locations when they are committed, and explain how cybercriminals should be delivered.
Improving spam email classification accuracy using ensemble techniques: a stacking approach

Abstract Spam emails pose a substantial cybersecurity danger, necessitating accurate classification to reduce unwanted messages and mitigate risks. This study focuses on enhancing spam email classification accuracy using stacking ensemble machine learning techniques. We trained and tested five classifiers: logistic regression, decision tree, K-nearest neighbors (KNN), Gaussian naive Bayes and AdaBoost. To address overfitting, two distinct datasets of spam emails were aggregated and balanced. Evaluating individual classifiers based on recall, precision and F1 score metrics revealed AdaBoost as the top performer. Considering evolving spam technology and new message types challenging traditional approaches, we propose a stacking method. By combining predictions from multiple base models, the stacking method aims to improve classification accuracy. The results demonstrate superior performance of the stacking method with the highest accuracy (98.8%), recall (98.8%) and F1 score (98.9%) among tested methods. Additional experiments validated our approach by varying dataset sizes and testing different classifier combinations. Our study presents an innovative combination of classifiers that significantly improves accuracy, contributing to the growing body of research on stacking techniques. Moreover, we compare classifier performances using a unique combination of two datasets, highlighting the potential of ensemble techniques, specifically stacking, in enhancing spam email classification accuracy. The implications extend beyond spam classification systems, offering insights applicable to other classification tasks. Continued research on emerging spam techniques is vital to ensure long-term effectiveness.
Constituent Attention for Vision Transformers

Multi-head self-attention (MSA) endows vision Transformers (ViTs) with the ability of modeling long-range interactions between tokens. However, recent works have uncovered that current arbitrary attention makes tokens distracted by extraneous dependencies, complicating the understanding of ViTs and hindering the overall optimization of ViTs. In this paper, we propose Constituent Attention (CA), a straightforward yet effective constraint on MSA to resolve aforementioned issues in different variants of ViTs with negligible overhead. Specifically, CA is designed to encourage the mutual attention between spatially connectable tokens via gathering such token pairs to the same group (termed constituent). Furthermore, with the layers going deeper in ViTs, small adjacent constituents are gradually merged into larger ones, exploring hierarchical structures in the visual objectives. Extensive experiments on four popular benchmarks demonstrate that CA assists the optimization of ViTs and contributes to significant performance gains especially at data-scarce scenarios. Moreover, the layer-wise reasoning mechanism aids ViTs in more consistent and progressive attention across different layers, rendering the inner workings easier to interpret. Our code is publicly available at https://github.com/zju-vipa/ConstituentAttention.
Sinhala-English Parallel Word Dictionary Dataset

Parallel datasets are vital for performing and evaluating any kind of multilingual task. However, in the cases where one of the considered language pairs is a low-resource language, the existing top-down parallel data such as corpora are lacking in both tally and quality due to the dearth of human annotation. Therefore, for low-resource languages, it is more feasible to move in the bottom-up direction where finer granular pairs such as dictionary datasets are developed first. They may then be used for mid-level tasks such as supervised multilingual word embedding alignment. These in turn can later guide higher-level tasks in the order of aligning sentence or paragraph text corpora used for Machine Translation (MT). Even though more approachable than generating and aligning a massive corpus for a low-resource language, for the same reason of apathy from larger research entities, even these finer granular data sets are lacking for some low-resource languages. We have observed that there is no free and open dictionary data set for the low-resource language, Sinhala. Thus, in this work, we introduce three parallel English-Sinhala word dictionaries (En-Si-dict-large, En-Si-dict-filtered, En-Si-dict-FastText) which help in multilingual Natural Language Processing (NLP) tasks related to English and Sinhala languages. In this paper, we explain the dataset creation pipeline as well as the experimental results of the tests we have carried out to verify the quality of the data sets. The data sets and the related scripts are available at https://github.com/kasunw22/sinhala-para-dict.
A Social Wallet Scheme with Robust Private Key Recovery

A social wallet is a type of digital wallet that stores contents such as crypto currencies with protection against the loss of private keys that guard access to the wallet. A significant barrier to the widespread use of digital wallets is the potential loss of the private key of a wallet and the consequent irrecoverable loss of wallet contents. The use of easy-to-remember private keys or easy-to-locate storing of private keys could result in the theft of private keys and wallet contents. A solution that allows the use of strong private keys along with a scheme to recover a lost private key is essential for the practical use of social wallets as a commodity service for non-specialist users. This paper presents the design, prototype implementation and security evaluation of a social wallet that incorporates biometric authentication and secret sharing techniques for its secure operation enabled through smart contracts.
SIT-Net: Surgical Image Translation Network Using a Generative Adversarial Method with Visual Feature Disentanglement

With flourishing advancements of surgical robots and learning-driven algorithms, surgical automation that enables robots with autonomous functions for certain subtasks by learning experts’ skills is attracting tremendous attention. To facilitate its development, introducing a high-quality surgical image translation method from current scenes to desired domains can not only augment the data for model training but also alleviate the problems that may frequently encounter using traditional approaches, like visual blurs or incomplete instrument generations. To this end, we propose a new surgical image generation method called Surgical Image Translation Network (SIT-Net). Considering complex features entangled within surgical images, a novel generator that leverages tool masks to disentangle the foreground and background characteristics is proposed. Besides, to retain the completeness of instruments, a novel loss function that can preserve tools’ features is introduced to further supervise the model training process. We extensively evaluated our method in terms of the visual appearance of translated images and adopted an evaluation indicator Fréchet Inception Distance (FID) to verify whether the generated images are close to the target domain or not. Both the qualitative and quantitative outcomes demonstrate that our SIT-Net is superior to the traditional approach regarding surgical image translation and generation in desired scenes.
Delayed Soft Actor-Critic Based Path Planning Method for UAV in Dense Obstacles Environment

In order to improve the convergence performance of soft actor-critic (SAC) algorithm in path planning problems, a delayed prioritized experience replay soft actor critic (DPERSAC) is proposed by designing a novel experience replay mechanism in a non-uniform manner for decreasing the convergence time. The path planning mathematical model is built for unmanned aerial vehicles (UAVs) subject to the flight performance constraints and obstacle avoidance constraints. Then the three typical elements of SAC are customized to satisfy the requirements of UAV's path planning. Differ from the traditional update manner that the soft Q-function network and policy network are updated recursively, the soft Q-function network is updated conditionally firstly and the policy network is subsequently iterated based on the trained soft Q-function in this paper. Finally, the Monte Carlo simulation results demonstrate that the computational time of the proposed DPERSAC method is only 4% of the rolling-based sparse A * algorithm in the dense obstacle environment.
DistFormer: A High-Accuracy Transformer-Based Collision Distance Estimator for Robotic Arms

Collision detection in trajectory planning of robotic arms is used to determine whether the path point is valid, which is a key part of the trajectory planning algorithm. Planning a path in a working environment requires tens of thousands of collision detections, so the speed of collision detection has a great influence on the planning time. To accelerate collision detection, several learning-based proxy collision detectors are proposed. However, they do not perform well in accuracy, resulting in unsafe paths. In this paper, we model collision detection as a sequence estimation problem for the first time, and propose a novel Transformer-based high-accuracy collision distance estimator, DistFormer, which can be directly embedded in the trajectory planner for collision detection. DistFormer models robotic arm and obstacles as bounding sequences to retain shape information, and uses encoder blocks based on self-attention mechanisms to aggregate point information into edge information. The cross-attention mechanism is then used to fuse the feature sequences of the robotic arm and the obstacles. Finally, the self-attention mechanism is used to convert the robot sequence fused with position information of obstacles into the collision distance. Extensive experiments have shown that DistFormer achieves higher accuracy than all baselines in different working environments.