Reviewerly/exHarmony huggingface dataset:
Ehnanced Grey Wolf Optimizer

In this paper, we propose EGWO, a new version of Grey Wolf Optimizer. The EGWO algorithm works in almost the same way as the original algorithm, only a simple tool was added into this algorithm. Both algorithms, original and also its new version, are tested on benchmark set of CEC2014 at three levels of dimension, 10, 30, and 50. Our results show that the implementation of our tool makes the Grey Wolf Optimizer significantly more effective in more than 64% of tested problems.
The Analysis of Optimizers in Training Artificial Neural Networks Using the Streaming Approach

One of the major challenges in modern artificial neural network training methods is reducing the learning time. To address this issue, a promising approach involves the continuous selection of the most crucial elements from the training set, utilizing data stream analysis. However, transitioning to this new learning paradigm raises several questions. In this study, we explore the significance of employing different optimizers for training neural networks using the streaming method. Experimental results are presented based on a detailed analysis of a convolutional neural network trained on the MNIST dataset.
Image Classification Through Graph Neural Networks and Random Walks

Humans can assimilate the context between information from a few perceived data, and this statement applies to the context in images. In machine learning, an image’s context can be represented and predicted using graph convolutional networks. In the literature, we saw hierarchies based on fully connected graphs that can simulate the image context. However, this method can be computationally costly and inaccurate, generating erroneous connection structure. This paper introduces a method based on random walks that can better represent the image context with precise connections, optimizing the number of edges and performing better results. We used pre-trained convolutional networks to extract features to introduce the GCN model. We performed two types of hierarchies, fully connected and random walk. We also performed a CNN model (end-to-end) for each literature pre-trained model used. Our method achieved better accuracies against the literature approaches. Moreover, it also considerably decreases the number of edges of the graph.
Fuzzy Hyperplane Based K-SVCR Multi-class Classification with Its Applications to Stock Prediction Problem

This study incorporates fuzzy set theory into K-SVCR (support vector classification regression for K-class classification) and proposes a novel formulation that is called a fuzzy hyperplane-based support vector classification regression for K-class classification (FH-K-SVCR). The main characteristics of the proposed FH-K-SVCR are that it assigns fuzzy membership degrees to every data vector according to the importance and the parameters for the hyperplane, such as the elements of normal vector and the bias term, are fuzzified variables. The proposed fuzzy hyperplane efficiently captures the ambiguous nature of real-world classification tasks by representing vagueness in the observed data set using fuzzy variables. The fuzzy hyperplane for the proposed FH-K-SVCR model significantly decreases the effect of noise. The experimental results for the real-world stock prediction application show that the proposed FH-K-SVCR model retains the advantages of a K-SVCR which achieves high classification performance in multi-classes classification task, and increases fault tolerance and robustness by using fuzzy set theory.
Controlled Refresh of the Population in Differential Evolution for Real-World Problems

In this paper, a new variant of the Differential Evolution (DE) algorithm is proposed to control the diversity of individuals in the population. The proposed approach is based on the failure of individuals in successive generations. The positions of the unsuccessful individuals are refreshed by employing the position parameters of the successful individuals from the population. Two control parameters of the proposed approach are studied to eliminate inappropriate settings. These nine variants of newly designed DE variants are compared with the classic DE algorithm when solving the set of real-world problems CEC 2011. The results show a very promising ability to solve real-world problems when the DE uses the proposed mechanism with the appropriate settings.
Dynamic Hand Gesture Recognition for Human-Robot Collaborative Assembly

In this work, we propose a novel framework for gesture recognition for human-robot collaborative assembly. It permits recognition of dynamic hand gestures and their duration to automate planning the assembly or common human-robot workspaces according to Methods-Time-Measurement recommendations. In the proposed approach the common workspace of a worker and Franka-Emika robot is observed by an overhead RGB camera. A spatio-temporal graph convolutional neural network operating on 3D hand joints extracted by MediaPipe is used to recognize hand motions in manual assembly tasks. It predicts five motion sequences: grasp, move, position, release, and reach. We present experimental results of gesture recognition achieved by a spatio-temporal graph convolutional neural network on real RGB image sequences.
Evaluating Augmented Reality, Deep Learning and Paper-Based Assistance Systems in Industrial Manual Assembly

Augmented reality or deep learning-based methods are increasingly being investigated for use in manufacturing tasks. As manual assembly tasks become more complex and change rapidly, these new technologies offer additional training, cognitive load reduction and error prevention. To realize their potential in industrial applications, technologically simple implementations and real-world experiments are needed. This study aims to explore the potential of deep learning-based object recognition as a driver for modular assembly assistance systems. The proposed deep learning framework and a state-of-the-art augmented reality head-mounted display will be compared for their potential to prevent errors and reduce training time in a real-world assembly task. To ensure that the results are transferable to industrial applications, experiments are conducted on the assembly of a complex product over a full working day of 8 h with a subject size of n = 20. It is found that both the proposed deep learning framework and the augmented reality display drastically reduce the error rate for certain types of errors.
Toward enriched decoding of mandarin spontaneous speech

A deep neural network (DNN)-based automatic speech recognition (ASR) method for enriched decoding of Mandarin spontaneous speech is proposed. It adopts an enhanced approach over the baseline model built with factored time delay neural networks (TDNN-f) and rescored with RNNLM to first building a baseline system composed of a TDNN-f acoustic model (AM), a trigram language model (LM), and a recurrent neural network language model (RNNLM) to generate a word lattice. It then sequentially incorporates a multi-task Part-of-Speech-RNNLM (POS-RNNLM), a hierarchical prosodic model (HPM), and a reduplication-word LM (RLM) into the decoding process by expanding the word lattice and performing rescoring to improve recognition performance and enrich the decoding output with syntactic parameters of POS and punctuation (PM), prosodic tags of word-juncture break types and syllable prosodic states, and an edited recognition text with reduplication words being eliminated. Experimental results on the Mandarin conversational dialogue corpus (MCDC) showed that SER, CER, and WER of 13.2 %, 13.9 %, and 19.1 % were achieved when incorporating the POS-RNNLM and HPM into the baseline system. They represented relative SER, CER, and WER reductions of 7.7 %, 7.9 % and 5.0 % as comparing with those of the baseline system. Futhermore, the use of the RLM resulted in additional 3 %, 4.6 %, and 4.5 % relative SER, CER, and WER reductions through eliminating reduplication words.
Generation of simulated data for Bengali text localization in natural images

In the domain of vision-based applications, the importance of text cannot be underestimated due to its natural capacity to provide accurate and comprehensive information. The application of scene text editing systems enables the modification and enhancement of textual material included in natural images while maintaining the integrity of the overall visual layout. The complexity of keeping the original background context and font styles when altering, however, is an extremely difficult challenge considering the changed image must perfectly blend with the original without being altered. This article contains significant simulated data on the dynamic features of digital image editing, advertising, content development, and related fields. The system comprises key components such as 2D simulated text on the styled image (is), text image (it), masking of text (maskt), real background image (tb), real sample image (tf), text skeleton (tsk), and text styled image (tt). The source dataset contains diverse components such as background images, color variations, fonts, and text content, while the synthetic dataset consists of 49,000 randomly generated images. The dataset provides both researchers and practitioners with a rich resource for identifying and evaluating these dynamic features. The dataset is publicly accessible via the link: https://data.mendeley.com/datasets/h9kry9y46s/3.
Cybercrime in India: An analysis of crime against women in ever expanding digital space

Abstract The ever expanding digital space and government initiatives like Digital India have increased connectivity, digitization, remote employment which empowered us with technology and made our lifestyle easy and fast but as digitization is enhancing, cyberattacks are proportionally growing. In the initial stage of this study, it was observed from the data analysis and it was evident that women are particularly a soft target of many cybercriminals and cyber fraudsters. Women feel insecure in cyberspace, hence there is a need to dive deeper into our understanding and statistics of cybercrimes against women in India. It becomes necessary to determine the factors that have led to the clear surge of such crimes in recent years. In recent studies no such analysis is done focusing on geographical factors and top most cybercrime types committed against women. Therefore, in this article, prediction for cybercrime trends against women was performed using statistical tools and techniques to provide a better insight into the current scenario revolving around cybercrimes and women. A regressive statistical analysis of cybercrime data of all states of India to understand the current trend of cybercrimes, identification of the most vulnerable states of India and specific cybercrime with their percentage in whole, also where cybercrime afflicted women stand in those numbers. Further, for preventive measures a robust guidelines is proposed to combat cybercrimes for a better future.
The problem of information systems security in SME

research-article Share on The problem of information systems security in SME Authors: Anatolie Alexei Telecommunication and Electronic Systems, Technical University of Moldova, Republic of Moldova Telecommunication and Electronic Systems, Technical University of Moldova, Republic of Moldova 0000-0002-0570-4854View Profile , Arina Alexei Telecommunication and Electronic Systems, Technical University of Moldova, Republic of Moldova Telecommunication and Electronic Systems, Technical University of Moldova, Republic of Moldova 0000-0003-4138-957XView Profile Authors Info & Claims CEEeGov '23: Proceedings of the Central and Eastern European eDem and eGov Days 2023September 2023Pages 101–105https://doi.org/10.1145/3603304.3603346Published:13 September 2023Publication History 0citation21DownloadsMetricsTotal Citations0Total Downloads21Last 12 Months21Last 6 weeks21 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
E-voting in Literature: Analyzing Nations’ Interest

Over the past two decades, we have seen how technological advancements have taken the world by storm. However, electronic voting (e-voting) machines are still not widely accepted. This paper analyzes research papers published on electronic voting in the Scopus database between 2000 and 2022. This research aims to analyze the number of studies conducted on e-voting systems and to determine whether the publication patterns match specific events on the timeline. The top five contributors to this research field (namely the United States, India, China, the United Kingdom, and Germany) were examined. Two of the main aspects of this paper are whether there is a trend between the number of papers published and the events occurring in a country and if more studies should be conducted regarding e-voting and its importance.
Transfer learning-based security protection framework for smart exercise monitoring

Sports exercise is very important for both physical and mental health, but improper exercise or equipment use may cause some security challenges. To address this issue, facing Internet of Things (IoT), currently, some video recognition algorithm-based smart security monitoring systems have been designed. However, existing video recognition algorithms usually assume that the data comes from the same collection device or follows the same distribution, resulting in ineffective handling of cross-camera or cross-device recognition problems. In this vein, this paper designed a transfer learning-based smart exercise monitoring security protection system, and proposed a new transfer learning-based video recognition framework, which consists of backbone network module, style transfer module, video feature aggregation module three parts and using this framework, two different models can be trained based on video face recognition dataset and video action recognition dataset, respectively, for identity recognition and action recognition. Experimental results show that the proposed framework can effectively handle video face recognition and video action recognition problems, which also demonstrates that our designed smart exercise monitoring security protection system can meet actual task requirements.
THE ATTITUDES OF AGRICULTURAL RESEARCHERS TOWARDS DATA SHARING: CASE STUDY OF THE REPUBLIC OF MOLDOVA

The implementation of information and communication technologies in research leads to a big data flow that needs efficient management. In the context of the Open Science movement and following FAIR principles, sharing of research data became an important issue for all research disciplines. In this work we focus on data sharing in agricultural sciences due to the significance of this field for every person, every country and even for the whole humanity. The paper is focused on data produced in agricultural sciences and provides information about types of data produced, on understanding the benefits from open data, provides perception and attitude of scholars towards sharing and reusing research data. The perceptions of agricultural researchers to data sharing are generally positive, but respondents reported a number of fears including lack of time, data security and losing control over intellectual property, possible misinterpretation and misuse of data. They also mentioned the interest in library assistance and the need of training in research data management.
A multi-sensor fusion method for static Chinese sign language recognition using DE–XGBoost

Static Chinese Sign Language Recognition (SCSLR) is an important field of research in human–computer interaction and assistive technology. Traditional SCSLR methods usually rely on computer vison sensors, which are susceptible to effects such as hand shapes, lighting conditions, and occlusions, resulting in low recognition accuracy. Additionally, sensor-based SCSLR methods cannot achieve high recognition accuracy due to limited hand gesture information. In this paper, we propose a multi-sensor fusion method, using a DE–XGBoost model, to fuse the information of hand gesture and finger curvature to achieve the SCSLR, which can overcome the recognition error problems caused by insufficient sign language information. In addition, we design and implement a prototype system, which consists of a smartphone and a smart glove, to evaluate our proposed method in comparison with support vector machine (SVM), XGBoost, gcForest, and artificial neural network (ANN). Experimental results show that our proposed method achieves a better performance in terms of accuracy, robustness, and real-time processing.
Lifelong deep learning‐based control of robot manipulators

Summary This study proposes a lifelong deep learning control scheme for robotic manipulators with bounded disturbances. This scheme involves the use of an online tunable deep neural network (DNN) to approximate the unknown nonlinear dynamics of the robot. The control scheme is developed by using a singular value decomposition‐based direct tracking error‐driven approach, which is utilized to derive the weight update laws for the DNN. To avoid catastrophic forgetting in multi‐task scenarios and to ensure lifelong learning (LL), a novel online LL scheme based on elastic weight consolidation is included in the DNN weight‐tuning laws. Our results demonstrate that the resulting closed‐loop system is uniformly ultimately bounded while the forgetting is reduced. To demonstrate the effectiveness of our approach, we provide simulation results comparing it with the conventional single‐layer NN approach and confirm its theoretical claims. The cumulative effect of the error and control input in the multitasking system shows a 43% improvement in performance by using the proposed LL‐based DNN control over recent literature.
Identification and reconstruction of anomalous data in dam monitoring considering temporal correlation

Abstract In dam monitoring, anomalous data is often removed directly by researchers. However, some anomalous data may be due to sudden changes in the state of the dam itself and should not be removed. In this study, anomalous data in dam monitoring is divided into two categories: anomalous error data caused by anomalies in the monitoring equipment, and anomalous warning data caused by sudden changes in the state of the dam itself. Then we propose a method for identifying and reconstructing anomalous data in dam monitoring that takes into account temporal correlation. This method is able to identify and retain anomalous warning data, while removing and reconstructing anomalous error data. To determine the temporal correlation between dam monitoring parameters (e.g. water level, horizontal displacement, etc), we use association rules, and to reconstruct the removed dam monitoring data in the case of an incomplete dataset, we propose a dam monitoring data reconstruction network (DMDRN) based on generative adversarial network. On this basis and in combination with the density-based spatial clustering of applications with noise algorithm, the types of anomalous data in dam monitoring are identified, and the anomalous error data is reconstructed based on DMDRN. Our approach has been successfully validated in two experiments to identify and reconstruct anomalous data at a particular dam in China.
A three-dimensional coverage path planning method for multi-UAV collaboration

In this paper, a new multi-UAV coverage path planning method is proposed for the scenario that UAVs need to completely search and cover a large area, which can plan an optimal feasible path that can avoid obstacles for multi-UAVs to cover the 3D space cooperatively. The method firstly decomposes the target space into mutually independent free cells by the cell decomposition algorithm; then each cell is covered individually according to the field of view of the UAV-mounted camera; then the task allocation among UAVs and the order of connecting cells are determined by comparing two classical task allocation algorithms, simulated annealing algorithm and genetic algorithm, with the total length of the path and the equilibrium degree among UAVs as the objective function; finally the task allocation among UAVs and the order of connecting cells are obtained by interpolation. Finally, the three-dimensional coverage path is obtained by interpolation. The experimental results show that the simulated annealing algorithm converges faster and with higher convergence accuracy than the genetic algorithm, and the path obtained by this method has the advantages of not being affected by terrain, multi-UAV collaboration and being able to avoid obstacles.
UDBNet: Detecting Unsafe Driving Behaviors Relevant to Urban Traffic Safety

Recently, the number of traffic accident deaths caused by unsafe driving has been increasing dramatically. At present, unsafe driving behavior detection in urban traffic remains a challenging task due to complex background, low spatial resolution, and poor illumination conditions etc., which limit generalization ability and low detection accuracy. In this paper, a novel detection network, called UDBNet, is proposed for unsafe driving behavior detection in complex traffic monitoring scenes. UDBNet introduces a deformable global convolution block to overcome the shortcomings of the existing Convolutional Neural Network (CNN) in capturing global features and adapting to deformed targets. In addition, UDBNet adopts a new Feature Pyramid Network (FPN), termed MBFPN, to enhance the ability of feature fusion. Finally, the effectiveness, robustness, and generalization of UDBNet are proved through extensive experiments.
Smilies: A Soft-Multi-Label-Guided Weakly Supervised Semantic Segmentation Framework for Remote Sensing Images

Weakly supervised semantic segmentation (WSSS) can well solve the problem of insufficient samples for semantic segmentation of remote sensing images. In order to better solve the problem of insufficient training samples, we introduce a Soft-MultI-Label-guIded wEakly Supervised semantic segmentation framework (SMILIES). It can generate pixel-level labels relatively under the supervision of few-shot image tag levels. We test it on the GID dataset (150 images) after training on only five images, and obtain 58.7% mIoU, which is higher than other WSSS methods when using few shot training samples. When we adopt image tag as supervision apply inferring on test data, our method has a better performance than fully-supervised DeepLabV3 with the same training samples. It can be inferred from the experiment that the SMILIES has better generalization performance and manual pixel-level labeling can benefit from it.
Dual encoder network with efficient channel attention refinement module for image splicing forgery detection

In the realm of picture forensics, it might be difficult to find and locate an image-splicing forgery. To improve the accuracy of the picture forensic evaluation, we introduce a dual encoder network (DAE-Net) with an efficient channel attention (ECA) module. The ECA module creates a fusion approach with an attention mechanism that enables the model to concentrate on local objects’ tampering characteristics and increases the accuracy of multi-region tampering identification. We suggest combining a dual-coding network with a multi-scale dilated convolutional feature fusion module to better detect small target tampering zones. Experimental evidence suggests that DAE-Net outperforms state-of-the-art methods. The attack experiments also demonstrate the DEA-Net model’s stability and noise resistance.