Miracle-dz/UniFAR-FTUs huggingface dataset:
Minced Meat Classification using Digital Imaging System Coupled with Machine Learning

Meat is widely recognized as a prevalent dietary choice due to its intricate nutritional profile, and its production constitutes a significant component of the global cattle industry. Due to the significant market demand for meat and the pursuit of maximizing profitability, meat products are often adulterated with additional ingredients. A diverse amount of meat can be effectively detected by the comparison model of machine learning. This study provides a comprehensive analysis of the performance of low-cost imaging for classifying minced meat product. In this study, a variety of raw minced meat, including beef, lamb, and pork, was utilized. We investigated the feasibility of low-cost imaging system coupled with machine learning classifier such as Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Logistic Regression, and AdaBoost Classifier. The findings indicate that the K-nearest neighbors (KNN) model exhibits superior performance, with an overall classification accuracy of 98.2%. The K-Nearest Neighbors (KNN) model exhibits a notable level of precision and accuracy across all classes. Therefore, this study confirms that machine learning algorithm provide robust features for classifying minced meat from the image data.
Adaptive Network-Based Pedestrian Detection in Haze Environment

The task of image dehazing, which involves the extraction of atmospheric haze from an image, presents a significant challenge due to the necessity of haze removal without detriment to the inherent quality of the image. The density of haze is subject to considerable variation, and as a result, extant dehazing algorithms frequently struggle to effectively manage differing haze densities. This often culminates in issues such as color distortion, loss of detail, and the emergence of other artifacts. In response to these challenges, this study introduces an innovative adaptive image dehazing algorithm. This algorithm employs a Generative Adversarial Network (GAN) as its foundational structure. Furthermore, an attention mechanism is integrated into the algorithm with the aim of preventing detail loss and concentrating on salient feature information. The proposed algorithm is subjected to rigorous evaluation across diverse datasets. The results demonstrate that it surpasses existing methods in terms of both image quality and robustness in relation to varying haze densities.
Rotated Object Detection of Ship in UAV Aerial Images Based on Improved YOLOv7

Aiming at the difficulty of ship detection in UA V aerial images and the problem of ignoring its direction, a rotated object detection algorithm based on improved YOLOv7 is proposed. By adding the angle prediction dimension and introducing the Gray Coded Label representation method to discretize the angle, the accuracy of the angle prediction is improved. The backbone network is replaced by Swin Transformer, and its attention mechanism is used to improve the feature extraction ability of the model. Then the K-means++ algorithm is used to cluster the anchor boxes, so that they are more suitable for the overall distribution to improve the detection performance of the model. The experimental results show that the improved rotated object detection method improves the detection accuracy by 9.12 percentage points compared with the original YOLOv7 model, and it can provide effective technical support for the task of ship detection.
Changes in the microphyll desert scrubland vegetation in an area under management

The aim of this study was to assess changes in the structure and floristic composition of the microphyll desert scrubland from the application of a technique of ecological restoration and the occurrence of natural fires in different periods in the Chihuahuan Desert. Through five treatments (control (MDMt), aerator roller applied in 2004 (RA04), 2008 (RA08), 2011 (RA11) and burned area in 2011 (IN11)), diversity and similarity between species were determined by Shannon and Sorensen indices as well as their importance value index (IVI). 28 tree and shrub species belonging to 14 families, among which Asteraceae, Fabaceae and Cactaceae noted for their abundance were recorded. The Shannon index revealed a higher richness (H’= 2.103) in the RA11 treatment while in the IN11, the shortest (H’ = 1.21). The greatest similarity was verified between treatments RA04 and RA08 76 % and otherwise between RA11 and RA08 with 44 % according to the Sorensen index. The Kruskal-Wallis test showed no significant difference between treatments respect to IVI (F = 2.463, df = 4, P = 0.261). The aerator roller treatment increased species richness at a short-term and dominance decreased substantially in Cactaceae and Agavaceae species, and favored Larrea tridentata coverage. The heat treatment reduced the coverage of the latter, of Jatropha dioica, Opuntia engelmannii and encouraged the presence and coverage of Viguiera stenoloba, Condalia spathulata and Ziziphus obstusifolia.
ICT Security through Games

With the increasing number of connected devices today, there is a growing reliance on digital technologies. This trend brings along an increased incidence of information and communication technology (ICT) security risks that are increasingly impacting modern societies’ running. The issue of ICT security is constantly rising and requires proactive measures to improve the protection of information infrastructure. The primary criterion is prevention, which aims to prepare users for emergencies to protect the privacy and confidentiality of information. Prevention usually focuses on educating and informing users about the safe use of ICT. These activities include coaching and training to identify risks and correctly use security protocols and tools. Gamification has found its use in this area, motivating users to raise cybersecurity awareness through game elements in non-game environments.
ArUco-based Automatic Extrinsic Sensor Calibration for Driverless Train System*

To realize driverless operation of railways on general lines, highly reliable forward monitoring devices are required. To ensure high reliability, a sensor fusion that integrates the detection results of multiple sensors is used, but in order to integrate properly, strict calibration of each sensor position and posture is required. In this research, a target based automatic extrinsic sensor calibration algorithm to obtain the relative pose transformation between camera and 3D Light Detection And Ranging (LiDAR) is proposed. The fiducial marker called ‘ArUco’ markers are used as a calibration target. The method employs ArUco marker feature points (corners as a feature) extraction simultaneously by the camera and LiDAR sensor data. Determining the relationship between the coordinates of the features of two data sets, a least square method with Single Value Decomposition (SVD) method for matrix formulation is utilized.
AfterImage: Evading Traditional Indicator of Compromise (IOC) Blocking

Malicious IP address blocking is a fundamental aspect of cybersecurity operations, and is used by security operators to decelerate the progress of network and host intrusions. From an intruders' standpoint, current methods to bypass IP address blocking typically involve setting up network proxies manually each time an existing IP address is detected and blocked. To automate this highly manual process, we propose AfterImage, an anti-forensics solution to automate the switching between multiple web proxies, thus simplifying the process of evading IP address blocking and logging defenses. Designed for local and remote deployment, AfterImage further includes the ability for automated discovery of usable, public web proxies through web scraping. By automating the setup of public-use proxies, AfterImage increases the efficiency of dissociating IP addresses during security testing. Our testing demonstrates the capacity for AfterImage to reestablish broken sessions over multiple proxies, as well as reliability of source IP concealment within redirected packets.
Sensitive Path oriented Malicious data Generation for Web applications

SQL injection always threatens the security of web applications. Static analysis and taint analysis are widely used to find vulnerable code paths, called sensitive paths, to detect SQL injection vulnerabilities. But false positives inevitably exists. To alleviate this problem, researchers try to generate test cases to cover sensitive paths. But it can only validate the feasibility of sensitive paths. This paper proposes a sensitive path-oriented malicious data generation to expose SQL injection vulnerabilities of sensitive paths on web applications. In more detail, taint analysis, genetic algorithm, and machine learning are integrated to generate malicious data to reveal SQL injection automatically. Taint analysis is used to find sensitive paths for Web applications. Based on sensitive paths’ historical attacked results by malicious data, a predictive model is constructed by machine learning to distinguish what malicious data is more likely to detect SQL injection vulnerability. A genetic algorithm is employed with the predictive model to generate malicious data to detect SQL injection vulnerability. The experiments are conducted on five open source web applications, and the results demonstrate that our approach is more efficient in malicious data generation for SQL injection vulnerability detection.
Drive with Quantified Risk: A Probabilistic Approach for Occlusion-Aware Trajectory Planning

Driving through traffic scenes with occlusions safely, efficiently and comfortably remains a huge challenge for autonomous vehicles (AVs), Blind spots are ubiquitous, from where emergencies may occur at any time, making AV hardly evade without sufficient precaution. In this study, we refer to the concept of phantom obstacles, and give them various aspects of uncertainties to ensure safety when facing driving scenarios with occlusions. A comprehensive lateral and longitudinal trajectory planning method is proposed based on stochastic model predictive control (SMPC) to mimic human-like precautionary driving styles. Furthermore, based on the cause analysis of deadlock situation, a quantified risks approach is proposed for occlusion-aware trajectory planning to formulate a non-conservative and deadlock-free driving strategy. Simulations for different objects in various typical scenarios demonstrate the safety, efficiency and universality of planning results.
UML Diagrams in Teaching Software Engineering Classes. A Case Study In Computer Science Class

Unified Modeling Language (UML) diagrams play an important role in the field of engineering, especially software development, due to their advantages such as accuracy and bridging the gap between documentation and code. However, there are different issues related to the field of specification, some of those issues are maintainability between code and documentation and efficiency of the UML models looking from the side of the developers. This paper describes a quick introspective between the differences of specific student groups, future software engineers, which worked with different UML diagrams and another group working without such diagrams. The analysis looks at the time, correctness of the code and understanding of the project from designing phase to developing phase. In the field of software engineering, UML diagrams play a vital role in designing, analyzing, and documenting software systems. Among the distinct types of UML diagrams, class diagrams stand out as a fundamental tool for representing the structure and behavior of a software application. This work aims to provide a concise overview of class diagrams and their significance in the implementation phase of software development.
CARNet: Channel Attention Guided Compression of Residual Network

As deep neural networks continue scaling up, the huge computational costs have become a major bottleneck for many real-world applications. In this paper, a Channel-Attention Residual Network (CARNet) is proposed to significantly reduce the complexity of ResNet models. By designing a lightweight attention module to recalibrate features, CARNet reduces 94.2% parameters (from 11.2M to 0.65M), 95% computations (from 32.6 GFLOPs to 1.63 GFLOPs), and 94.2% model size (from 42.8MB to 2.55MB) compared to original ResNet, while maintaining comparable accuracy on the same dataset. More importantly, the proposed CARNet demonstrates a promising direction to enable attention mechanism for computer vision models on resource constrained platforms. The extreme reduction in model complexity allows attention-based models to be deployed on edge devices for the first time. This work reveals the possibility of combining visual attention with lightweight designs and makes an important step towards bringing human-like visual recognition capability to real-world applications such as augmented reality, surveillance, and mobile robotics. The code and models are open sourced for reproducing the results: https://github.com/Faiyong/CARNet.
Trajectory Tracking MPC with Online Model Adaptation Using Gaussian Process Regression and Clustering

This paper presents an Adaptive Model Predictive Controller (AMPC) for vehicle trajectory tracking. The proposed approach combines Model Predictive Control (MPC) with an online parameter learning algorithm based on a Gaussian Process Regression (GPR). The goal is to improve the tracking accuracy caused by model errors and condition changes. The vehicle's yaw intensification is considered as the adaptive parameter. Compared to prior research, clustered data dictionaries are introduced for the parameter, which are updated periodically according to the trajectory dynamics and the tracking performance. The data dictionaries allow the AMPC to memorize multiple driving conditions and to resort quickly to a previously learned parameter value if similar conditions are detected. The approach is evaluated in a simulation study based on CarMaker and Matlab/Simulink, where the vehicle is tested along several different closed-loop circuits with additional weight to emulate condition changes. These experiments demonstrate that the AMPC is able to adapt to repeated condition changes and to transfer learned behavior to unknown trajectories.
Study of First and Third Person Viewpoints in Virtual Environments: Physiological and Performance Measurements

Studies on virtual reality and their relationship with human factors have been limited in Indonesia. Yet, in the context of Industry 4.0, the concept of virtualization places 3D simulation at the forefront of project decision-making. Physical ergonomics and physiology explore how the human body responds to external stimuli, encompassing factors like heart rate, brain waves, fatigue levels, and overall performance. These elements are critical because they influence interactions within the work system. In this study, we conducted two experiments to investigate user perspectives within virtual environments, specifically focusing on first- and third-person viewpoints. The first experiment assessed the impact of these viewpoints on the user's physiological factors, including heart and brain wave patterns. The results indicated that participants did not experience a significant increase in workload based on heart and brain wave data, and they reported no symptoms, as indicated by a symptom questionnaire. In the second experiment, participants were exposed to a critical simulation video from both first- and third-person viewpoints. The results revealed that the first-person perspective significantly improved task completion time within the simulation. These findings are essential for stakeholders in the field of virtual reality, helping them determine when to employ first- or third-person viewpoints in their applications.
Digital Signature Chain for Security and Integrity Data Transmission

Digital signatures have been widely used by both private and government agencies. However, the use of chain digital signatures is still not widely used, especially in the military world, this results in a lack of ability to ensure data integrity, where it will be difficult to find out who has made changes to the document and to find out the original source of the document. This paper proposes a digital signature chain as a solution to guarantee data authenticity and prevent tampering during transmission. This technique involves creating a chain of digital signatures that are attached to data before it is sent over the network. The proposed method is expected to provide a more secure and efficient way to ensure data integrity, compared to traditional methods such as encryption and checksums. This paper evaluates the effectiveness of the proposed method through a series of experiments and shows that digital signature chains are an effective and reliable way to secure and maintain data transmission over networks. The proposed research aims to evaluate the effectiveness of digital signature chain technology in ensuring data security and integrity and to provide recommendations for its implementation.
SEAFL: Transforming Federated Learning for Enhanced Privacy in 6G-Enabled Vehicles

The utilization of artificial intelligence (AI) within the 6G-enabled Internet of Vehicles (IoV) has proven to be crucial in tackling complex issues, primarily through the adoption of deep learning methodologies. However, in certain sensitive data-driven sectors like healthcare and autopilot systems, traditional centralised training raises privacy concerns. To address this, federated learning (FL) has emerged as a popular approach, enabling collaborative model learning without exposing local data. However, recent investigations have highlighted that malevolent actors can exploit shared parameters to undermine critical applications such as autonomous driving systems, medical data from wearable devices, emergency message propagation scenarios, and the decision-making processes of industrial robots. In this article, we introduce a novel and robust aggregation mechanism named Secure and Efficient Aggregation for Federated Learning (SEAFL), tailored to the context of the 6G-enabled Internet of Vehicles (IoV). This approach aims to address the increasing privacy concerns and security threats arising in the collaborative learning landscape of the IoV, where sensitive data privacy is of paramount importance. Within the context of SEAFL, we amalgamated local differential privacy (LDP) into the realm of federated learning, employing a three-tier aggregation protocol within the software-defined vehicular fog (SDVF) architecture. Leveraging the incorporation of multiple aggregation levels and a privacy budget, SEAFL enhances privacy and accuracy by iteratively perturbing and aggregating gradients, resulting in heightened privacy protection, minimised gradient exposure, preserved data integrity, improved global model accuracy, and fortified resilience against adversarial threats. Through comprehensive experiments conducted on attack datasets, SEAFL outperforms alternative FL-based Local Differential Privacy (LDP) solutions, showcasing superior performance in terms of both accuracy and efficiency. This makes SEAFL a promising approach for secure and efficient federated learning in sensitive vehicular applications like emergency message dissemination scenarios.
Intrusion Detection Method Based on BiGRU and CBAM

With the increase of social informatization, network security incidents occur frequently, and the security problem is paid more and more attention. Network intrusion detection is one of the important components of network security. Aiming at the current problems of data imbalance and low detection accuracy of anomalous traffic, this paper proposes an intrusion detection method based on BiGRU and CBAM. First, we use ADASYN adaptive oversampling and class balancing to oversample a few classes of anomalous data to solve the data imbalance problem. After that, we learn the temporal features of the data through a bidirectional long and short-term memory network, we introduce spatial attention and temporal attention CBAM, and the fused features are assigned different weights, and finally output the detection results. Experimental tests are conducted on the NSL-KDD dataset, and the results show that the model in this paper has higher accuracy and lower false detection rate compared with the current machine learning and deep learning models.
Generic Simulation Framework for Evaluation Process: Applied to AI-powered Visual Perception System in Autonomous Driving

The rapid development of modern vehicle technology necessitates a robust and comprehensive evaluation framework for AI-powered systems in Autonomous Driving Systems (ADS). This paper introduces a generic simulation framework tailored to the evaluation process of AI-powered systems in ADS, with an emphasis on the visual perception system as a case study. The framework integrates different levels of evaluation that contain critical metrics, providing a comprehensive understanding of system performance and targeted evaluation and analysis of components, under challenging driving conditions. This information can be used to guide improvements to the system, such as selecting better AI algorithms, modifying the design of the system, or improving the environment in which it operates. For each component, the framework not only incorporates a unified definition and configuration but also establishes a communication mechanism, which contributes to effectively integrating different tools and platforms into the evaluation process. The insights gleaned from the visual perception systems case study can be leveraged for adapting and applying the generic evaluation process to other AI-powered systems in ADS, aiming to promote the development of more reliable and safer systems.
Computer Aided Diagnosis of Depression Using EEG Signal Processing

Major depressive disorder (MDD) emerges as a prominent factor leading to disability on a global scale and contributes significantly to the global burden of illness overall. The traditional method of detecting MDD is by continuous medical examination by a psychologist or psychiatrist. Our objective is to develop a non-invasive device which collects brain signals from the head and gets interfaced with a computer. The purpose of this paper is to develop a computer-aided diagnosis system that can identify depression in real time. The proposed system comprises three main components: the ADS1299 Front-End (FE) Printed Development Kit (PDK) evaluation board, a wearable electrode, and a desktop application. The primary approach involves the utilization of electroencephalogram (EEG) signal processing, along with the ADS1299 FE PDK evaluation board and deep learning techniques. This paper involves the utilisation of a publicly available dataset for training the deep learning model. The convolutional Neural Network (CNN) algorithm is used for the classification process. Absolute and relative powers are computed, and an asymmetry image matrix is generated based on the relative power values. By analysing the image matrix, the system can classify a patient as healthy or suffering from major depression based on higher or lower relative power, respectively. This paper seeks to make a valuable contribution to the academic sphere of the study of mental health diagnosis by leveraging advanced signal processing techniques and deep learning models for more accurate and efficient detection of depression.