Reviewerly/exHarmony huggingface dataset:
Evaluation and Analysis Data from Twitter Data By Using Hybrid CNN &amp; LTSM

The central theme for this thesis is the design of an aspect-based sentiment analysis model for the classification of online Italian automotive forums' comments. The work starts with designing a strategy for collecting information about target forums to make it possible to develop a machine learning-based sentiment classification model. The study involved applying the CNN and LTSM model, a state-of-the-art solution based on a parametric model that will improve the performance of a baseline algorithm, especially in case of very noisy data like the ones where this tool is supposed to be to work on. This work has been designed as a two-stage CNN and LTSM classifier in all its parts. It was compared with a one-step classifier to detect the pertinence about some topics, and eventually, the sentiment achieved an accuracy of 96.78 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> for all comments. The current problem passed from a typical three degrees' polarity sentiment analysis to a four labels text classification, where it will be introduced an additional category for determining whether the text is pertinent to a particular topic or not. Presenting this information, the models must be enhanced, and a cascade classification solution will be proposed. The final model is then utilized for a real-world use case. New data have been classified concerning some selected topics, finally presented exploiting a data visualization but still not satisfactory, thus making sentiment analysis an ongoing and open research subject.
Real-Time Hardware Acceleration of Low Precision Quantized Custom Neural Network Model on ZYNQ SoC

Achieving a lower memory footprint and reduced computational density in neural network models requires the use of low-precision models. However, existing techniques typically rely on floating-point arithmetic to preserve accuracy, which can be problematic for convolutional neural network models (CNNs) with substantial memory requirements when using floating-point numbers. Additionally, larger bit widths lead to higher computational density in hardware architectures. This has resulted in the need for current models to become deeper network models with sometimes billions, of parameters to address contemporary problems, thereby increasing computational complexity and causing memory allocation issues. These challenges render existing hardware accelerators insufficient. In scenarios where hardware complexity can be traded-off for accuracy, the adoption of model quantization enables the utilization of limited hardware resources for implementing neural networks. From a hardware design standpoint, employing quantized models offers notable advantages in terms of speed, memory utilization, and power consumption compared to traditional floating-point arithmetic. To this end, we propose a method for detecting network intrusions by quantizing weight and activation functions using the Brevitas library in a custom multi-layer detector. We conducted real-time experimentation of the technique on the ZYNQ System-on-Chip (SoC) using the FINN framework, which enabled deep neural network extraction within Field Programmable Gate Arrays (FPGAs), resulting in an accuracy rate of approximately 92%. We selected the UNSW-NB15 dataset, which was generated by the Australian Cyber Security Center (ACCS), for the investigation.
Experimental Comparison of Global Planners for Trajectory Planning of Mobile Robots in an Unknown Environment with Dynamic Obstacles

Interacting with their workspace, detecting obstacles, and real-time path planning in the field to reach the goal in the unknown environment are current working areas of mobile robotics. Working in an unknown environment with dynamic obstacles is a challenging topic for autonomous robots, which need to protect themselves from the dynamic obstacles they encounter, not damage the obstacle, and re-plan their path according to their new position. This study performed an experimental comparison of three different global planners for trajectory planning of mobile robots in an unknown environment with dynamic obstacles. Global Planner, Carrot Planner, and NavfnROS planners in the ROS package were used as global planners in two different mazes with dynamic obstacles. The durations from the start to the goal point in the two designed environments were determined for each global planner.
Electronic Voting Through Blockchain: A Survey

As technology advances and more of our lives are digitised, elections are no exception. This paper proposes a survey of the current state of the art blockchain voting protocols by performing a systematic literature review and evaluating 25 selected papers. One of the main objectives of the surveyed proposals was to maintain voter anonymity while ensuring vote verifiability. However, most systems fell short in terms of scalability, as they would not be able to handle nationwide elections. Additionally, some proposals identified security vulnerabilities during security testing. Nevertheless, there were promising developments in terms of performance and security through Layer 2 solutions. These solutions offer more flexibility in system design and enable features that ensure anonymity and scalability.
Impact of Artificial and Physical Locomotion Techniques on Cybersickness, Usability, Immersion

One of the immersive technologies that entered our lives with Metaverse is virtual reality (VR). A large number of new displays and input devices are used with this technology. With these devices, movement is provided in the virtual environment. Locomotion, also called motion in a virtual environment, is a VR technology that allows users to move freely in a virtual environment over long distances without leaving their current position. However, this technology is causing cybersickness (CS) in users, and new ideas are constantly coming up to solve movement-related problems. In this study, artificial and physical locomotions were examined regarding CS. CS is defined as discomfort during movements unrelated to body movement, for example, in airplanes, boats, or land travel. In the study conducted with 45 participants, the “Omnidirectional Walking”, “Real Walking”, “Teleport” and “Continuous Touch” locomotions were tested for CS, usability, and immersion. Nausea, oculomotor, disorientation, and total score parameters were measured with the Simulator Sickness Questionnaire (SSQ). It has been observed that “Continuous Touch” from artificial locomotion at the CS point produces greater discomfort but lowers the level of immersion. Significant usability differences were found between physical and artificial locomotion. Physical locomotion offered higher levels of engagement and immersion. It is essential to focus on these issues, especially in finding solutions to potential problems that may arise in UX designs for virtual environments.
Albanian Authorship Attribution Model

Authorship attribution (AA) is a subfield of NLP that analyzes the author's prior works to determine who wrote a text based on its features. Each natural language has its characteristics, just like every author's unique writing style. This study aims to conduct an in-depth comparison of several AA machine-learning techniques. The specially created Albanian corpus (A3C) and the English dataset (Reuters C50) have been used in the experiments. Using n-grams, we perform character-level and word-level analyses of the text to represent the author's writing style. We use five different classification algorithms to train the AA models. The TF-IDF feature vector is used as input to the models. Various experiments were conducted on the corpora. The most accurate results were obtained using word n-grams after stopword removal. The SVM algorithm performed best on the A3C dataset (Albanian). We get a 95% F1 score using SVM. On the C50 dataset (English), the SVM classifier achieved an 83% F1 score. Experiments have provided evidence of the models' robust performance on the AA corpora.
Intelligent Method for UAV Navigation and De-confliction --Powered by Multi-Agent Reinforcement Learning

As Uncrewed Aircraft Systems (UAS) become more ubiquitous in urban airspace around the world, the need for reliable navigation and de-confliction technologies becomes paramount. In this paper, the authors improve the popular Deep Reinforcement Learning (RL) methods of Twin Delayed DDPG (TD3) and Proximal Policy Optimization (PPO) and propose two new integrated algorithms for de-confliction with single and multiple intruder UASs in different cases of fixed and variable altitudes. Based on the Actor-Critic method, new RL systems and reward functions are designed that enhance the training efficiency of the navigating UAS agent for the considered environment models. The simulation results show the capability of the trained agent to successfully navigate in a complex environment amid fixed and velocity obstacles. This research contributes to the development of autonomous navigation for UAS in urban airspace.
The possibility of blockchain application in Higher Education

Blockchain technology has the potential to revolutionize the way Higher Education operates. Blockchain is a decentralized and secure database that can store and manage digital information without the need for a centralized authority. This technology can be applied in Higher Education to improve the security, transparency, and efficiency of academic and administrative processes such as student records, credentials verification, and payment processing. Additionally, blockchainbased platforms can provide new opportunities for collaboration and credentialing that could enhance the learning experience for students. While there are still some challenges to be overcome, such as regulatory issues and the need for interoperability, the potential benefits of blockchain in Higher Education are significant and worth exploring. The purpose of this paper is to investigate the possible applications of blockchain technology in Higher Education institutions and to provide a proposal/scheme of a blockchain-based academic and career record system in Higher Education. The system being proposed is composed of three layers, which are the data, blockchain, and application layers. It is distinguished by decentralized control, secure transactions, and privacy protection.
Anomaly Detection on Univariate Sensing Time Series Data for Smart Aquaculture Using K-Means, Isolation Forest, and Local Outlier Factor

Aquaculture has a great importance in economic development and food production. Maintaining an ecological environment with good water quality is the most critical link to ensure the efficient and qualitative of aquaculture. Good management of the water quality can avoid occurrence of abnormal conditions and significantly contribute to secure food in the future. Detection of anomalies ensures that the aquaculture environment is maintained properly to meet healthy and proper requirements for fish farming. The main focus of this paper is the use of machine learning approaches to detect anomalies for water quality data in aquaculture environment. It presents an analysis of three machine learning anomaly detection techniques: the K-Means clustering, the Local Outlier Factor, and the Isolation Forest. Extensive analysis of the mentioned techniques was conducted using several sensor datasets obtained from a real-world IoT aquaculture system, specifically for the parameters of temperature, dissolved oxygen, and pH. The evaluation analysis reveals that K-Means and Isolation Forest anomaly detection methods show promising results in detecting anomalies for the three aquaculture parameters.
Preamble-based RF-DNA Fingerprinting Under Varying Temperatures

A total of 30.9 billion Internet of Things (loT) deployments are expected by 2025 with most employing weak or no encryption at all, which raises concerns about loT security. This concern is exacerbated by loT-connected critical infrastructure and the successful exploitation of this security vulnerability. This led researchers to propose a physical layer-based loT security solution coined Specific Emitter Identification (SEI). However, SEI has been shown to be sensitive to temperature changes. This sensitivity is important when considering loT deployments in highly variable temperature environments. The presented approach shows the temperature sensitivity of SEI is mitigated when the classifier is trained using RF-DNA fingerprints drawn from waveforms collected at two temperatures. In fact, SEI performance improves the most when the two temperatures are at or near the extremes of the operating temperature range. Specifically, our work shows that training SEI classifiers using the extremes of the collected temperatures improves overall classification performance across temperature ranges. The work in this paper also shows that emitters operating in a sub-ambient, exothermic state have a more consistent fingerprint than those operating in a high-temperature, endothermic state.
Coordinated Multi-Robot Exploration using Reinforcement Learning

Exploring an unknown environment by multiple autonomous robots is a long-studied problem in robotics. The agents need to coordinate the exploration to minimize the overlapping region and avoid interference with each other. This is particularly challenging in decentralized execution, where no central system guides the exploration. In such scenarios, agents need to incorporate temporal planning and the intentions of other agents into the decision-making process. In this work, we focus on several challenges involved in multi-UAV exploration in unseen, unstructured, and cluttered environments. Consequently, we propose a Multi-Agent Reinforcement Learning (MARL) based framework wherein agents learn the effective strategy to allocate and explore the environment. We evaluate the performance of our proposed framework in terms of average distance traveled, percentage of redundant exploration, and the rate of exploration against a classical approach.
AI-assisted Software Engineering: a tertiary study

The research in Artificial Intelligence (AI) and its applications across the software engineering (SE) domain has progressed significantly in the last decade, evidenced by an increase in systematic literature reviews. Our study aims to provide an overview of existing systematic reviews within this research area and synthesize the findings related to AI subfields (Machine Learning, Search-based, Evolutionary Computation, Vision, Robotics, Expert Systems, Speech Processing, Natural Language Processing, and Planning) supporting the five main SE processes: requirements, design, development, testing, and maintenance. For our tertiary study, we used the methodology based on Kitchenham's guidelines. We selected 11 reviews published between 2000 and 2021, including results from 513 primary studies. The selected reviews cover the AI subfields across the five main SE processes. Studies have covered Machine Learning, Natural Language Processing, and Evolutionary Computations as AI subfields assisting SE processes. We have found that reviews in AI-assisted software testing are the most common, followed by software maintenance and development. Our study can help researchers identify missing reviews on AI-assisted SE topics to help further consolidate this research area.
An Integrated Real-time UAV Trajectory Optimization and Potential Field Approach for Dynamic Collision Avoidance

This paper presents an integrated approach that combines trajectory optimization and Artificial Potential Field (APF) method for real-time optimal Unmanned Aerial Vehicle (UAV) trajectory planning and dynamic collision avoidance. A minimum-time trajectory optimization problem is formulated with initial and final positions as boundary conditions and collision avoidance as constraints. It is transcribed into a nonlinear programming problem using Chebyshev pseudospectral method. The state and control histories are approximated by using Lagrange polynomials and the collocation points are used to satisfy constraints. A novel sigmoid-type collision avoidance constraint is proposed to overcome the drawbacks of Lagrange polynomial approximation in pseudospectral methods that only guarantees inequality constraint satisfaction only at nodal points. Automatic differentiation of cost function and constraints is used to quickly determine their gradient and Jacobian, respectively. An APF method is used to update the optimal control inputs for guaranteeing collision avoidance. The trajectory optimization and APF method are implemented in a closed-loop fashion continuously, but in parallel at moderate and high frequencies, respectively. The initial guess for the optimization is provided based on the previous solution. The proposed approach is tested and validated through indoor experiments.Experiment video link: https://youtu.be/swSspfvYjJs
Automatic Hate Speech Detection using Natural Language Processing: A state-of-the-art literature review

Social network usage is growing daily, making it impossible to manage the enormous amount of data being generated. The presence of abusive behavior and hate speech is a clearly harmful phenomenon that is evident on these networks. Due to its importance, recent studies have revealed a significant concern in this field. This review aims to provide insight into the tasks and procedures associated with the automatic detection and classification of texts containing hate speech. As the domain of hate speech is wide, an analysis of definitions is conducted and a comprehensive and unifying definition is proposed. This paper also investigates the latest datasets across languages used to train AI models. Recent studies show that feature selection plays a key role in detecting hate speech. In this research, we analyze which are the most utilized and impactful features from works done in this domain. While various classification algorithms have been used for hate speech detection, we investigate numerous research studies using multiple types of machine learning and deep learning models and present the most recent and relevant methods.
Autonomous Control of UAV for Proximity Tracking of Ground Vehicles with AprilTag and Feedforward Control

Recently, various tracking flight techniques of unmanned aerial vehicle (UAVs) have been developed and used in various applications. However, a proximity tracking flight is still challenging because accurate estimation of the position and velocity of a target ground vehicle (GV) is difficult. This paper presents an autonomous UAV system that can fly close to GVs. If the relative position between the UAV and GV was used for flight control and velocity was not used, the tracking can be unsuccessful. To address this issue, the speed of the ground vehicle was also estimated, and it was feedforwarded into the control loop. Real flight experiments showed that this approach greatly improved the tracking performance; the UAV tracked the GV driving at approximately 4 m/s with an average displacement error of less than 1 m.
Overview of UAV Trajectory Planning for High-Speed Flight

The use of autonomous unmanned aerial vehicles has increased for High-Speed flights, leading to the need for improved performance. Trajectory planning is the primary approach to achieving high speeds, as it is safer and more flexible than other planning types. Some approaches include polynomial trajectories, optimization-based, search-based, sampling-based, and artificial intelligence, mainly reinforcement learning. This paper provides an overview of the main techniques for high-speed trajectory planning in UAVs and the challenges associated with them. It also describes essential UAV dynamics, control, and perception to reach high speeds. These techniques are demonstrated in several missions and environments, describing their methodologies. Finally, we discuss the open problems and potential future research directions in this field.
Path Planning for Air-Ground Robot Considering Modal Switching Point Optimization

An innovative sort of mobility platform that can both drive and fly is the air-ground robot. The need for an agile flight cannot be satisfied by traditional path planning techniques for air-ground robots. Prior studies had mostly focused on improving the energy efficiency of paths, seldom taking the seeking speed and optimizing take-off and landing places into account. A robot for the field application environment was proposed, and a lightweight global spatial planning technique for the robot based on the graph-search algorithm taking mode switching point optimization into account, with an emphasis on energy efficiency, searching speed, and the viability of real deployment. The fundamental concept is to lower the computational burden by employing an interchangeable search approach that combines planar and spatial search. Furthermore, to safeguard the health of the power battery and the integrity of the mission execution, a trap escape approach was also provided. Simulations are run to test the effectiveness of the suggested model based on the field DEM map. The simulation results show that our technology is capable of producing finished, plausible 3D paths with a high degree of believability. Additionally, the mode-switching point optimization method efficiently identifies additional acceptable places for mode switching, and the improved paths use less time and energy.
A low cost and effective multi-instance abnormal driving behavior detection system under edge computing

The rapid development of intelligent transportation system (ITS) has brought great convenience to people's life, and also makes various intelligent vehicle services emerge in an endless stream. However, with more than 1.35 million people killed in traffic accidents every year, the detection of abnormal driving behavior has attracted more and more attention. Although some progress has been made in the research of abnormal driving behavior detection, numerous challenges remain. Firstly, it is difficult to manually analyze whether driving behavior is abnormal, which makes it costly to accurately label them for machine learning. Secondly, few studies have considered the problem of class imbalance in the abnormal driving behavior detection, which is common in driving behavior and has a significant negative impact on the detection model, especially in weakly supervised scenarios. Thirdly, assessing a driver's behavior solely based on real-time performance is insufficient, the historical behavior is equally important. To address these challenges, an abnormal driving behavior detection system based on weakly supervised learning under edge computing architecture is proposed in this paper. This system considers the class imbalance problem and utilizes the historical information of the vehicles to provide a low cost and effective detection service. Firstly, the abnormal driving behavior detection is modeled as a multi-instance learning (MIL) problem, which only needs coarse-grained labels for anomaly detection, so that the labeling work becomes low cost. Secondly, the class imbalance problem under weakly supervised learning is solved by incorporating parameter transfer and pre-estimating abnormal scores. This approach enhances the model's sensitivity to rare classes and significantly improves the performance of weakly supervised detection models. Thirdly, in order to learn the historical information of the vehicle, a summary mechanism is used for the road side units (RSUs), which can provide different historical summary of the vehicles for other detection nodes, so as to make the model more effective for different abnormal events. Several experiments are carried out on both simulation dataset and real-world dataset to verify the performance of the proposed method. The results demonstrate that compared with the traditional supervised learning methods, the proposed method can still achieve near-optimal performance when only using coarse-grained labels, and all the strategies proposed have improved the detection performance obviously.