Reviewerly/exHarmony huggingface dataset:
Chinese medical named entity recognition based on fusion of global features and multi-local features

Chinese medical Named Entity Recognition (NER) is a task of Natural Language Processing (NLP), which aims to extract key information from Chinese medical texts. Recently, Transformer becomes the mainstream approach for NLP because of its powerful global feature extraction capability. However, entities usually appear in the form of subsequences in NER, therefore the local features are not negligible, and the uncertainty of Chinese word segmentation increases the difficulty of this task. In this paper, we propose a network structure that combines global feature extraction and multi-local feature extraction to enhance the performance of Chinese medical NER. Based on the global feature extraction by Transformer, we propose to use Bi-LSTM with a context integration mechanism to extract multi-local features to enhance the local semantic information of sequences, which integrates contextual information from the future and the past into each cell, and generates different weights through the gate mechanism to enhance the representational ability of each cell and thus the semantic information of the local sequences. And a feature fusion method based on attention mechanism is proposed, which allows the decoder to better focus on the more important information for predicting the current character. During the global feature extraction, the flat-lattice structure is introduced to generate all the potential results of Chinese word segmentation, and a span-based relative position coding is generated to capture the sequence characteristics. Finally, a CRF with conditional constraints is used as the decoder of the model. Experimental results on two benchmark datasets show the effectiveness of our model, and the method significantly outperforms the state-of-the-art methods in the medical NER task, achieving <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 value of 93.64% on CCKS2017 and 85.01% on CCKS2019.
Developing and designing the e-Bosy reservation system for ICT equipment

E-bosy booking system was designed as a reservation system for information and communications technology (ICT) equipment in Universiti Malaysia Kelantan (UMK), Malaysia. Currently, all the reservation systems are done manually, through filling out the form or through email. Using this existing system faces numerous challenges, such as the absence of knowledge of available spaces, tediousness improper management of reservations, clashes in reservations, communication failures, and missing the submitted reservation form. Thus, the development and design of the e-Bosy system (electronic booking system) were intended to bring the efficiency and friendly user of reservation systems both at home and aboard. The system basic functions are designed according to the general requirement including user’s registration and login, online equipment booking, personal center, centrege board and database construction. Through e-BoSy the system, every reservation has been recorded for the future improvement of the services.
A Robust Bio-inspired Color Detection Algorithm Under Low/High Light and Shadow for UAV Applications

This project presents a bio-inspired color detection algorithm designed to keep good performance in scenarios where the light conditions are variable and challenging. It is based on the theory of opposite colors, studied in the context of human vision, where the ability to detect colors is associated with a neural response to contrast. To validate the algorithm, we have performed three experiments under adverse luminosity conditions in an indoor environment. Then, we compared the processed images analyzing them in two color spaces - YCrCb and CIEXYZ. As a result, we identified that the YCrCb space provided superior responses and allowed the detection of all eight colors chosen for the experiments.
Mechanism for Enhancement of Functionality in Deep Echo State Network by Optimizing Leaking Rate

Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, that can respond to layer-specific different time-scales. This dynamical characteristic leads to enhance performance of ESN. However, neither the design guidelines for the hyperparameters of the network and individual neurons nor the mechanism to produce the diverse dynamical response have been clarified. In this study, we proposed an approach to generate the dynamical responses with different time-scales in each layer by adjusting the leaking rate of neurons. Through the evaluations time-series prediction task for different leaking rates, multiscale entropy analysis for each reservoir layer, and cross-correlation between adjacent layers, we found that when the leaking rate is set to low, the layer-specific dynamics with different time-scales are generated, as well as a mechanism whereby the signal propagates to subsequent layers with a delay, i.e., queue characteristic. These characteristics produce a high memory capacity. Consequently, diverse responses with delay leads to an enhancement of Deep-ESN functionality.
Detecting DeepFakes: A Deep Convolutional Neural Network Approach with Depth Wise Separable Convolutions

This paper presents a deep learning based approach for detecting deepfake images and videos. With the rise of free and easily accessible software tools, such as GANs, creating deep-fakes has become effortless. However, detecting these deepfakes has proven to be a significant challenge. Our method uses a deep convolutional neural network architecture that involves depth-wise separable convolutions to classify whether an image or video is real or fake. We trained and evaluated our model on the CelebDF V2 dataset, achieving high accuracy rates of 98.8% and 97.4% for image and video classification, respectively. Our work is a significant contribution towards mitigating the spread of deepfakes, and we plan to expand our research to detect AI-generated audio in future work. We also propose the development of an online browser extension to make our detection method accessible to the general public and to integrate it into various social media and messaging platforms to prevent the spread of deepfakes.
CAPTCHA Recognition And Analysis Using Custom Based CNN Model - Capsecure

CAPTCHAs are automated tests designed to distin-guish between computers and humans, attacking programs, or other computerised agents that attempt to imitate human intel-ligence. The main intent of this research is to develop a method to crack CAPTCHA using a custom based convolutional neural network (CNN) model called CAP-SECURE. The proposed model aims to distinguish or tell web sites about the weaknesses and vulnerabilities of the CAPTCHAs. The CAP-SECURE model is based on sequential CNN model and it outperforms the existing CNN architecture like VGG-16 and ALEX-net. The model has the potential to solve and explore both numerical and alphanumerical CAPTCHAs. For developing an efficient model, a dataset of 200000 CAPTCHAs has been generated to train our model. In this exposition, we study CNN based deep neural network model to meet the current challenges, and provide solutions to deal with the issues regarding CAPTCHAs. The network cracking accuracy is shown to be 94.67 percent for alpha-numerical test dataset. Compared to traditional deep learning methods, the proposed custom based model has a better recognition rate and robustness.
Visual Tracking of Suspicious Entities through Unmanned Aerial Vehicles in Conflicted Areas, Structures and Urban Regions

In response to security and surveillance needs, unmanned aerial vehicles are being employed to carry out monitoring and target identification in conflict scenarios, such as areas dominated by criminal factions, structures under siege by terrorists, and zones of armed conflict. And having the motivation to contribute with solutions to solve these needs, our research presents a tracking application that integrates object detection with a region-based convolutional neural network as a detector of targets of interest and the discriminative correlation filter with Channel and Reliability Spatial as a tracking algorithm for the proposed method. This approach has the objective and the motivation to help the preventive actions of security systems used in conflicted areas, in structures, and in urban regions. The generated model results show an average accuracy of 99.8% for the object tracker when applied to the video sequences in the image dataset.
Augmented Reality system for Immersive Mobile Robot Simulation and Trajectory Estimation

In augmented reality (AR) applications, the real world can interact with the virtual world through image processing and overlay. This concept is increasingly being utilized in various Industry 4.0 applications, such as mobile robotics, the Internet of Things, computer vision, and more. This article presents the development of an augmented reality (AR)-based digital twin system with a focus on its educational significance. The developed system enables simulation, interaction, and real-time visualization of the trajectory of an autonomous mobile robot, using sensor fusion with the Kalman filter. By integrating AR with the robot's odometry, the system enhances the accuracy of robot localization, providing an immersive and interactive learning experience. Leveraging AR software development kits like Unity and Vuforia facilitated the creation of a modular application that can be used in conjunction with a mobile robot. The effectiveness of the developed system is demonstrated by delivering enhanced reliability and precision in estimating the robot's trajectory, thereby contributing to a better understanding of autonomous locomotion principles. Furthermore, the article discusses the educational applications and ongoing improvements of the platform, exploring the potential of augmented reality as a powerful educational tool in the context of mobile robotics. Index Terms–Augmented reality. mobile robotics, digital twin, educational applications.
GOBEAT: Towards a Methodology to Support MAS Test Case Definition

Multi-Agent Systems (MAS) is a branch of Artificial Intelligence (AI) that works with distributed systems whose components are autonomous entities called agents. The soccer game has been used as a test bed to stimulate research in the MAS. A soccer team, i.e., a MAS, is composed of a group of players, i.e., agents, who should coordinate their actions towards a goal. The testing process in MASs has proven to be challenging. Autonomous agents are programmed to learn during their execution. So, running the same test scenario successively can lead to different results. It makes difficult the application of conventional software testing techniques. This paper proposes the Goal Behavioral Agent Testing (GoBeAT) methodology to assist in specifying the MAS test suite applied to the robot soccer domain. GoBeAT was tested in a case study by a robot soccer team of the Robotic World Cup (RoboCup) and showed positive results in the definition of a test suit.
Real-Time Pedestrian Detection and Tracking System Using Deep Learning and Kalman filter: Applications on Embedded Systems in Advanced Driver Assistance Systems.

In this paper, we present a perception system for assisting robotic vehicles in smart cities, facilitating interaction with pedestrians, cyclists, and other motor vehicles while adhering to local traffic rules, all with the aim of enhancing traffic safety. Multiple Object Tracking (MOT) is a complex and fundamental problem in computer vision for robotic vehicles, requiring individual evaluation of various detected mobile agents to make informed decisions. To address this challenge, we utilize embedded and dedicated hardware systems, along with Deep Learning algorithms, as powerful tools for realtime processing of computer vision. In this work, we developed an Advanced Driver Assistance System (ADAS) with 91.85% (mAP) and 78.2% (IoU) accuracy for MOT using Nvidia’s Jetson-Nano and optimized the Deep-SORT YOLOv7 model in conjunction with the Kalman filter algorithm to achieve this capability, and a rate equal to or greater than 50% is already considered relevant for the task of detecting dynamic obstacles.
Leveraging Compute Heterogeneity in Federated Multi-Task Classification

One of the main challenges of federated learning (FL) algorithms is resource heterogeneity, which may prevent participants with limited computing capabilities from being able to effectively participate in the learning process. The presence of such participants can significantly impede the training process and cause notable degradation in the overall system performance. In this paper, we propose a set of policies for leveraging computing heterogeneity, with the aim of accelerating the training of federated multi-task classification based on support vector machine (SVM). We evaluate the effectiveness of the proposed policies in various regimes and draw conclusions on their applicability to different scenarios. Our results indicate a significant improvement in training time and model performance, especially in cases where the computing resources are highly heterogeneous.
A Ubiquitous Learning Approach on Robotics

Ubiquitous learning refers to the integration of learning processes with everyday environments and activities using technology. By leveraging ubiquitous learning principles in the field of robotics, we can foster an immersive and interactive learning environment that promotes continuous learning and knowledge acquisition. This paper presents an in-depth exploration of a ubiquitous learning approach for robotics with the aim to enhance the educational experiences and capabilities of robotic systems. Furthermore, it explores the potential benefits and challenges of ubiquitous learning in the field of robotics, such as increased adaptability, personalized learning experiences, and the development of lifelong learning skills. The results indicate that a ubiquitous learning approach can significantly enhance the learning capabilities of students.
DMRBNet: Dilated Multi-Scale Residual Block-Based Deep Network for Detection of Breast Cancer from MRI Images

Breast cancer (BC) is a common type of cancer that develops from breast tissue cells. Early detection is critical, and mammography is an important tool for this. A biopsy is indicated for lesions with a risk of malignancy of more than 2%, however, only a tiny number of them are confirmed to be malignant. Magnetic Resonance Imaging (MRI) is employed to eliminate unneeded biopsies, but it is a sophisticated and time-consuming operation requiring specialized knowledge. To improve breast cancer diagnosis, a computer-aided diagnostic system using MRI images was developed. The system utilizes a novel neural network called dilated multi-scale residual block-based convolutional neural network (DMRBNet), which effectively extracts features from various image regions. Compared to seven recent advanced approaches, DMRBNet demonstrated superior performance on the BC-MRI dataset. The accuracy of the network is 98.57%, and the error rate is 0.1005. These findings highlight its potential for medical and industrial applications in breast cancer detection.
Classification of breast ultrasound images in BI-RADS categories using binary decomposition strategies with convolutional neural networks

The Breast Imaging Reporting & Data System (BI-RADS) assesses breast findings in terms of categories of risk of malignancy from which the clinical conduct of patients is determined. In particular, categories 2 and 3 are related to benign tumors, recommending routine imaging studies. In contrast, categories 4 and 5 are associated with suspicious tumors analyzed by biopsy exam to determine the pathology. Recently, this multiclass classification problem has been addressed by a single convolutional neural network (CNN) model that provides a posterior probability to each BI-RADS category. Nevertheless, multiclass classification can be improved by using binary decomposition strategies that map a multiclass problem on several two-class problems. This paper compares three binary decomposition strategies applied to breast ultrasound (BUS) image classification: one-versus-all (OVA), one-versus-one (OVO), and all-and-one (A&O), where the CNN Xception model is used as a base learner. Five BI-RADS classes are considered: categories 2, 3, and 4 in benign tumors and categories 4 and 5 in malignant tumors. The results show that the OVA strategy presents the best classification performance regarding OVO and A&O with a general accuracy of 60%, while a single CNN model reaches 57%. Besides, OVA obtains a sensitivity and specificity of 0.80 and 0.93, while a single CNN attains 0.77 and 0.94. Hence, dividing the multiclass classification problem into simpler binary classification sub-problems improves BUS classification.
Evaluation of Face Recognition Systems for RoboCup@Home Service Robots

Face recognition technology is becoming increasingly important in a variety of applications. In the context of service robotics, accurate and efficient face recognition has big potential to improve overall performance and capabilities, including the ability to create more natural human-robot interactions. This paper presents an in-depth study of state-of-the-art face detection and recognition algorithms, focusing on their use in the RoboCup@Home competition. Several face recognition architectures were evaluated, including DeepFace, ArcFace and FaceNet, considering two alternative implementations of the latter. The methods were first tested on two state-of-the-art datasets, and then, to test them under competition-like conditions, they were tested on images captured in real time, taking into account different situations, such as lighting, use of accessories, face position, and multiple faces in the same scene. Results show that Esler's implementation of FaceNet would perform better on RoboCup@Home challenges.
Enhancing Identity Management: Exploring the Potential of Blockchain Technology

Blockchain technology presents a promising solution for improving identity management systems, and overcoming the shortcomings of traditional methods. This research investigates the application of blockchain in identity management, with a specific focus on the self-sovereign model that empowers individuals to exert authority over their own data. The decentralized and transparent nature of blockchain, coupled with cryptographic techniques and consensus mechanisms, ensures the integrity and security of identity information. Additionally, blockchain offers advantages such as enhanced security, transparency, and individual data control. The study proposes a high-level algorithm for establishing and accessing identities on the blockchain and evaluates existing blockchain-based identity management systems. In summary, blockchain technology has the potential to revolutionize identity management by granting individuals greater control over their data, while simultaneously enhancing security and efficiency in the process.
Efficient Web Application Mapping During the Reconnaissance Phase of Penetration Testing

This paper presents the ptwebdiscover, an advanced tool designed for web application mapping in the reconnaissance phase of penetration testing. Unlike most available tools, it operates using multiple threads, boasts modularity, and enhances testing capabilities. Notable features include the implementation of a brute force method for searching shorter file names and the ability to mark specific locations in tested URLs for string insertion. The paper provides comprehensive details about the tool's multi-threaded implementation, intelligent backup searches, and comparisons with other web application penetration testing tools. Furthermore, ptwebdiscover has been seamlessly integrated into the Penterep platform, providing a significant advantage during manual web application penetration testing.
Safe Crane Handling with Collision-Free Path Planning using DDPG

Operating cranes can be an extremely challenging task due to the low visibility or blind spots in the work area. These problems with the operator visibility causes an alarming number of crane accidents. To improve crane operations, this study aims to develop a collision-free path planning model for crane manipulation, these simulators and training techniques offer a cost-effective and environmentally friendly way to conduct training exercises, without the need for real-life scenarios that could damage the equipment, harm workers or disrupt ongoing operations.In order to assist crane operations, this study aims to develop a collision-free path planning model for crane manipulation. Given that, this is a complex problem with challenging data acquisition, so the use of methodologies employed in supervised and unsupervised learning becomes impractical, as they require prior data for model training and testing.Hence, for this work’s development, it was decided to use reinforcement learning (RL) techniques. In RL algorithms the agent learns to take actions to maximize an accumulated reward over time, aiming to learn the best strategy of action in a given situation interacting with the environment to which it is inserted.During our evaluation of the Deep Deterministic Policy Gradient (DDPG) algorithm, we considered two distinct reward methods: an adapted reward and a proposed reward. Our analysis revealed that the performance of the proposed reward method outperformed that of the adapted method, displaying notably enhanced training efficacy. These findings underscore the considerable advantages of incorporating the suggested reward into our approach.
Multi-label Bengali Abusive Comments Classification using Problem Transformation Method

Sentiment analysis on the Bengali language is limited to binary and multi-class classifications. This paper focuses on the multi-label classification of the Bengali abusive social media comments dataset having 10220 rows of different social media negative comments using the problem transformation method. The texts are categorized into five labels: toxic, threat, obscene, insult, and racism. Three different multi-label approaches: Binary Relevance, Label Powerset, and Classifier Chain combined with three popular machine learning algorithms: Multinomial Naive Bayes, Random Forest, and Logistic Regression are implemented in this research. This study evaluates the performance of these models by applying them to various classification scenarios, considering five-label, four-label, three-label, and two-label classifications independently. Our research outcomes are evaluated with some performance indicators: Accuracy, Precision, Recall, F1-Scores, Confusion matrix, Macro-average, and Hamming score. The combination of Label Powerset and Logistic Regression outperformed the other two classifier-model combinations because of their quick adaptation to the dataset with an accuracy of 88.07% for five label classifications, 90.12% for four label classifications, 90.56% considering three label classifications and 92.67% for two label classification individually. With 88.07% accuracy, the Label Powerset with Logistic Regression approach can be applied to classify five types of sentiment in a single text.