Reviewerly/exHarmony huggingface dataset:
Edge Deployment of Vision-Based Model for Human Following Robot

Mobile robots are proliferating at a significant pace and the continuous interaction between humans and robots opens the doors to facilitate our daily life activities. Following the target person with the robot is an important human-robot interaction (HRI) task that leads to its applications in industrial, domestic, and medical assistant robots. To implement the robotic tasks, traditional solutions rely on cloud servers that cause significant communication overhead due to data offloading. In our work, we overcome this potential issue of cloud-based solutions, by implementing the task of a hum-following robot (HFR) at the Nvidia Jetson Xavier NX edge platform. To perform the HFR task, typical approaches track the target person only from behind. While, our work allows the robot to track the person from behind, front, and side views (left & right). In this article, we combine the latest advances of deep learning and metric learning by presenting two trackers: Single Person Head Detection-based Tracking (SPHDT) model and Single Person full-Body Detection-based Tracking (SPBDT) model. For both models, we leverage a deep learning-based single object detector called MobileNetSSD with a metric learning-based re-identification model, DaSiamRPN. We perform the qualitative analysis considering six major environmental factors: pose change, illumination variations, partial occlusion, full occlusion, wall corner, and different viewing angles. Based on the better performance of SPBDT, compared to SPHDT in the experimental results, we select SPBDT model for the robot to track the target. We also use this vision model to provide the relative position, location, distance, and angle of the target person to control the robot's movement for performing the human-following task.
RDIU-Net: Lightweight Medical Image Segmentation Network

In recent years, medical image segmentation using deep learning methods has become more and more popular and developed with the aim of both reducing human-related errors and the time required for manual segmentation. One of the pioneers in deep learning-based biological image segmentation networks, U-Net was proposed back in 2015. Since then, several models have been proposed to extend U-Net. However, the trade-off between computational complexity and accuracy remains a major challenge. To address this trade-off, we use a new Involution kernel for spatial information and propose a model lightweight medical image segmentation network, Residual Involution U-Net (RDIU-Net). Involution, Residual, and Dense structures are incorporated into the U-Net model to extract both channel and spatial features. Evaluations have been carried out on three different datasets of ultrasound, X-ray, and dermoscopic images. The proposed model RDIU-Net showed superior results in accuracy, processing speed, training stability, and convergence compared to U-Net.
Human Flow Measurement System Using Floor Estimation of Depth Images for Low-End IoT Devices

We propose a system that uses depth information, which represents the distance from the sensor, instead of color information to do both measure human flow and protect privacy for low-end IoT devices. The system is designed to detect the position and number of persons from depth information. Since administrative organizations or educational institutions have been reducing their budgets, this system should be implemented at as low a cost as possible. In order to realize human flow detection system on low-end Iot devices, we use low performance depth cameras as data acquisition device controlled by single board computers, such as Raspberry Pi. As one of our goals is all data processing are performed on single board computers, we adopt computational methods for the detection as possible as simple. The proposed method is based on the background subtraction method, which prepares a reference depth image and extracts moving regions from one frame extracted from the video depth image. Furthermore, we aim to achieve high-precision people flow measurement by combining the following three elements: segmentation of moving objects using edge information, identification of human areas using floor information, and human tracking using areas where people overlap in the direction of the time axis. Experiments were also conducted and evaluated in a real space using a program that implements the presented method.
Sparse Bayesian Network-Based Disturbance Observer for Policy-Based Reinforcement Learning

We proposed the Sparse Bayesian Network-based Disturbance Observer (SBN-DOB) to enhance the robustness of policy-based reinforcement learning. SBN-DOB utilizes sparse Bayesian learning to estimate the nominal inverse model dynamics, effectively mitigating model uncertainty and disturbances without relying on physical modeling. The SBN-DOB can be compressed by inducing sparsity in network parameters through sparse Bayesian learning, and the Bayesian model reduces the risk of overfitting during inference. To evaluate the effectiveness of the proposed approach, We conducted experiments using the policy network (PN) of the soft actor-critic algorithm in combination with SBN-DOB for six control tasks with an uncertain environment. The results of these experiments demonstrate that the performance of PN is preserved against continuous disturbances and state noise even with compression applied to SBN-DOB. Consequently, SBN-DOB is expected to minimize the simulation-to-reality gap of reinforcement learning by used in embedded systems with limited performance and capacity.
AWP: Alpha Wrapping-Based Collision-Free Surface-Along Path Planner for UAV

This paper proposes a novel alpha wrapping-based collision-free surface-along path planner (AWP) designed specifically for unmanned aerial vehicles (UAVs) navigating along complex structural surfaces. The major objective is to facilitate collision-free movement of UAVs along such surfaces. The proposed methodology entails three key steps. Firstly, the alpha wrapping algorithm generates a free 3-dimensional curve, which smoothly carves and refines a mesh to approximate the desired structure. This process efficiently creates a free and smooth space for UAV maneuverability. Secondly, the axis-aligned bounding box (AABB) tree is constructed based on the mesh representation of the free space. The AABB tree identifies the points nearest to the UAV's start and target points within the free space. Lastly, a discrete geodesic problem solver determines the shortest path between the identified nearest points. The discrete geodesic problem solver considers the continuous characteristics of the surface represented by the mesh. The proposed AWP takes advantage of mesh-based path planning, thereby providing UAVs with collision-free surface-along paths. The experimental results demonstrate the proposed method's efficiency and robustness in generating collision-free paths for UAVs when traversing complex structures.
Reinforcement Learning based Sequential Multi-Robot Task Allocation Considering Weight of Objects and Payload of Robots

Recently, many studies have been conducted that apply deep reinforcement learning to multi-robot task allocation. However, most of them are in the form of distributing the same number of tasks to robots, making it difficult to respond to irregular tasks. In addition, the weight of the object and the payload of the robot cannot be set differently. In this paper, we proposed reinforcement learning based sequential multi robot task allocation algorithm considering weight of objects and payload of robots. This algorithm is designed for use in environments with irregularly occurring tasks, and sequentially assigns the most suitable robot for the tasks that occur. Also, this algorithm has the advantage that it can be applied even if robots have different payloads and objects have different weights. Rainbow DQN was used for this algorithm, and the learning environment was customized using openAI gym.
Fuzzy Dynamic Sliding Mode Control for Permanent Magnet Synchronous Generator Model

This paper investigates the fuzzy control problem for nonlinear permanent magnet synchronous motor (PMSM) model via dynamic sliding-mode method. The nonlinear PMSM model has equivalently expressed into linear sub models via the Takagi-Sugeno fuzzy approach based on suitable membership rules. The key advantage of the developed approach is that a very restrictive assumptions in most existing sliding mode control approaches for fuzzy systems have been removed. This paper employs the dynamic sliding mode scheme to control nonlinear PMSM. In the established sliding-mode control scheme, the sliding surface function is formed linearly with the system states and control inputs. Then, a fuzzy dynamic term is utilized to construct the sliding-mode feedback controller. Thus, sufficient conditions are proposed to make the sliding surface reachable with the existence of the system perturbations to make the augmented system stable. Finally, the applicability of designed dynamic sliding methodology is demonstrated by a controller design for the nonlinear PMSM model.
Explainable offline automatic signature verifier to support forensic handwriting examiners

Abstract Signature verification is a critical task in many applications, including forensic science, legal judgments, and financial markets. However, current signature verification systems are often difficult to explain, which can limit their acceptance in these applications. In this paper, we propose a novel explainable offline automatic signature verifier (ASV) to support forensic handwriting examiners. Our ASV is based on a universal background model (UBM) constructed from offline signature images. It allows us to assign a questioned signature to the UBM and to a reference set of known signatures using simple distance measures. This makes it possible to explain the verifier’s decision in a way that is understandable to non-experts. We evaluated our ASV on publicly available databases and found that it achieves competitive performance with state-of-the-art ASVs, even when challenging 1 versus 1 comparisons are considered. Our results demonstrate that it is possible to develop an explainable ASV that is also competitive in terms of performance. We believe that our ASV has the potential to improve the acceptance of signature verification in critical applications such as forensic science and legal judgments.
Aerial Manipulator Interaction with the Environment

This chapter investigates the problem of an aerial manipulator interacting with the environment. The chapter is split into two parts. The former considers an aerial device with tilting propellers that, thanks to a super-twisting slide mode controller, can control the interaction force for inspection task purposes. The latter proposes a hardware-in-the-loop simulator for human cooperation and environmental interaction with an aerial manipulator. This part includes the mathematical background and theoretical derivation with insights into the relative stability proofs. Simulations in a highly realistic environment endowed with a physics engine and real experiments validate both the proposed approaches.
Autonomous Multi-rotor Unmanned Aerial Vehicles for Tactical Coverage

This chapter presents an original guidance system for autonomous multi-rotor unmanned aerial vehicles (UAVs) equipped with forward-facing cameras and tasked with creating maps of unknown environments while operating in a tactical manner and at very low altitudes. The few existing guidance systems for UAVs operating in potentially hazardous environments essentially assume direct information on the location and the kind of potential threat to the aircraft, do not account for the UAV’s dynamics, and usually assumes that the UAV operates at high altitudes. The proposed guidance system, on the contrary, assumes no prior information on the environment and does not rely on external sources of information. Furthermore, to enable operations at low altitudes and in cluttered environments, the proposed guidance system includes a fast trajectory planner. For these features, UAV employing this guidance system can be employed by first responders and other emergency units to collect real-time data about a given location. Several unique features distinguish the proposed guidance system, including an original algorithm to cover connected set, which allows users to prioritize accuracy over flight time, an original algorithm to produce convex constraint sets in real time from voxel maps, and original approaches to induce tactical behaviors both in the optimization-based path planner and the model predictive control-based trajectory planner underlying the proposed guidance system. Numerical simulations validate the applicability and the effectiveness of the proposed guidance system.
All the world’s a (hyper)graph: A data drama

Abstract We introduce Hyperbard, a dataset of diverse relational data representations derived from Shakespeare’s plays. Our representations range from simple graphs capturing character co-occurrence in single scenes to hypergraphs encoding complex communication settings and character contributions as hyperedges with edge-specific node weights. By making multiple intuitive representations readily available for experimentation, we facilitate rigorous representation robustness checks in graph learning, graph mining, and network analysis, highlighting the advantages and drawbacks of specific representations. Leveraging the data released in Hyperbard, we demonstrate that many solutions to popular graph mining problems are highly dependent on the representation choice, thus calling current graph curation practices into question. As an homage to our data source, and asserting that science can also be art, we present our points in the form of a play.1
A Novel Knowledge Distillation Technique for Colonoscopy and Medical Image Segmentation

Since medical equipment has limited resources and little computational capacity, large segmentation models cannot be installed on them. Colonoscopy equipment, which has little computer power for deep learning models, is one such example where large segmentation models cannot be embedded. The solution to this problem is to perform model compression on cutting-edge models that have demonstrated outstanding diagnostic and prediction capabilities while performing segmentation. Knowledge distillation of large medical image segmentation models is the main focus of our study. However, unlike other knowledge distillation papers, we find the best student and teacher pair with varied network architecture. Even though the network architectures of our student and teacher pairs are different, the pairs still achieve better results in terms of segmentation score, pixel accuracy intersection-over-union (IoU, Jaccard Index), and dice coefficient (F1 Score). Overfitting is one of the main issues with this methodology, which we were able to minimize through depth-wise model pruning and hyperparameter tuning. Finally, we achieved the Xception-Efficientb0 pair as knowledge distillation which can outperform state-of-the-art models’ values of performance metrics on Kvasir-SEG and CVC-ClinicDB datasets.
AI-Enabled Smart Monitoring Device for Image Capturing and Recognition

This paper presents an AI-powered Smart Monitoring Device for real-time applications with multiple capabilities, including case 1: face recognition for age, gender, and vehicle registration plate (or commonly known as number plate) recognition. This proposed system has been integrated into the AI-based Smart Identification Engine and uses an IP-enabled camera to capture real-time information at public places. We use AI to create a convolutional neural network (CNN), a Python-based deep learning module that extracts streams, identifies and processes images, and collects snapshots from videos before storing the data in the cloud. Case 2: The AI-enabled Smart Digital Monitoring Engine (SDME) is an implementation of the CNN deep learning model to identify and process images and videos, and can be used in various sectors such as targeted advertising in malls and automated identification in gated communities through face and vehicle number plate recognition.
Vision-Based Facial Detection and Recognition for Attendance System Using Reinforcement Learning

In this paper, we propose a reinforcement learning (RL)-based attendance system (RLAS) for marking attendance of the students presented during a class using the frames captured by a video camera. The RLAS comprises of agent module and the environment module. In the agent module, we fine-tune the multi- task cascaded convolution network (MTCNN) module and the ArcFace module to identify the students present in class. The MTCNN module consists of two neural networks, the P-Network (P-Net) and the R-Network (R-Net). In the P-Net, we add 2 convolutional layers for extracting the latent features from the facial images of the students. Similarly, we modify the R-Net by adding two dense layers for detecting the bounding boxes from the frames captured by the video camera. Based on the latent features obtained from the fine-tuned MTCNN, the ArcFace identifies the students present in the class. The environment module of the RLAS uses the reward function to evaluate the output generated from the agent module. If the agent module correctly identifies all the students presented in the frames captured by the camera, then the reward function marks the attendance to those students. Else, the environment module back-propagates the error obtained from the reward function to the agent module. To evaluate the RLAS, we created a dataset of 1, 20, 000 different images of 2400 students studying at our university. In our experimental evaluation, we observed that the fine-tuned MTCNN along with the ArcFace provides the transfer learning mechanism to the RLAS. Therefore, the RLAS obtains less time complexity than the different variants of the MTCNN and CNN models.
Detection of Malicious URLs Using Gradient Boosting Classifier

The Internet has integrated seamlessly into our daily lives. The use of Internet depends on individual requirements and goals. It has also provided opportunities to perform activities which are malicious such as spamming, spyware, phishing, etc. Phishers try to deceive the users by social engineering or creating mockup or compromised websites which are sent the users mail or through SMS. When these URLs are clicked, the information such as account ID, username, password, and many other details are stolen from individuals and organizations which can lead to reputation damage, infrastructure damage, etc. Although many methods have been proposed to detect the websites which are malicious, the cyberattackers have evolved their methods to pretend to be trustworthy. In order to detect these malicious attacks, the concept of machine learning is used. This paper proposes that the gradient boosting classifier, and XGBoost classifier outperformed than other machine learning models. The gradient boost classifier machine learning model is stored and used for detection of URLs (generous or malignant) and resulted an accuracy of 97.4%.
Transformer-Based Attention Model for Email Spam Classification

Over the past few decades, communication has become easier due to the rapid development in technology. Although several modes exist for communication, in this era of Internet, electronic mail or email, turn out to be very popular because of its effectiveness, inexpensive, and easy to use for personal communication as well as business purposes and sharing important information in the form of text, images, documents, etc. to others. This proficiency leads to email being exposed to numerous attacks together with spamming. At present, spam email is a major source of concern for email users where unbidden messages, used for business purposes, are directed extensively to several mailing lists, entities, or newsgroups. These spam emails are used for the purpose of advertising products, collecting personal information, sending destructive contents in the form of executable file to outbreak user systems or the link to malicious website to steal confidential data such as hacking bank accounts, passwords leading to reduction in efficiency, security threats, consume server storage space, and unessential consumption of network bandwidth. Currently, there are 47.3% spam emails out of all emails and henceforth it become necessary to build a competent spam filters to categorize and block spam email. In order to enhance the accuracy of the model, natural language processing is used. In the proposed framework, efficacy of word embedding is offered to categorize the spam emails. It fine-tunes the pretrained Bidirectional Encoder Representations from Transformers (BERT) model to classify the legitimate emails and spam emails. Attention layer is used by the BERT model to incorporate the context of the text into that perspective. The outcomes are compared with minimum Redundancy Maximum Relevance (mRMR), Artificial Neural Network, Recurrent Neural Network, and Long Short-Term Memory (LSTM) for Lingspam, Enron, and Spamassassin dataset. The proposed method accomplished the uppermost correctness of 97 and 98% F1-score.
Comparative Study of Keccak SHA-3 Implementations

This paper conducts an extensive comparative study of state-of-the-art solutions for implementing the SHA-3 hash function. SHA-3, a pivotal component in modern cryptography, has spawned numerous implementations across diverse platforms and technologies. This research aims to provide valuable insights into selecting and optimizing Keccak SHA-3 implementations. Our study encompasses an in-depth analysis of hardware, software, and software–hardware (hybrid) solutions. We assess the strengths, weaknesses, and performance metrics of each approach. Critical factors, including computational efficiency, scalability, and flexibility, are evaluated across different use cases. We investigate how each implementation performs in terms of speed and resource utilization. This research aims to improve the knowledge of cryptographic systems, aiding in the informed design and deployment of efficient cryptographic solutions. By providing a comprehensive overview of SHA-3 implementations, this study offers a clear understanding of the available options and equips professionals and researchers with the necessary insights to make informed decisions in their cryptographic endeavors.
Empirical Review of Oversampling Methods to Handle the Class Imbalance Problem

Many real-world applications, including fault detection and medical diagnostics, suffer from the Class Imbalance Problem (CIP), in which one class (the majority class) has more instances than the other (the minority class). CIP affects the classifier’s learning as they are based on optimizing accuracy over all training instances. As a result, these classifiers will tend to label all the instances as belonging to the majority class. To handle the CIP, we have reviewed six oversampling methods: SMOTE (Synthetic Minority Oversampling Technique), Random SMOTE (RS), Distance SMOTE (DS), Borderline SMOTE2 (BS2), Polynom fit SMOTE (PS) and Modified SMOTE (MSMOTE) using four classification models: Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes (NB) and Random Forest (RF). We have used Area under the curve (AUC) to compare the performance of the classifiers using different oversampling methods. For the majority of the datasets, it was found that the Polynom fit SMOTE method performed better than the other oversampling methods.