Reviewerly/exHarmony huggingface dataset:
The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.
Multi-Objective Island Model Genetic Programming for Predicting the Stokes Flow around a Sphere

This paper is aimed at enhancing the success rate of Genetic Programming (GP) algorithms for symbolic regressions. It is shown that the outcome of GP algorithms over several runs can lead to an optimal solution for such problems, but the success rate, i.e., the number of successful runs, is sometimes small. We address this issue by proposing multi-objective and island model (IM) optimization for GP. We study the influence of various objective functions and IM configurations on the success rates and present 36 algorithm variants, which are tasked with solving two benchmark equations from the fluid mechanics area. This specific benchmark problem has been previously shown to suffer from a low success rate and high variations between the results of multiple runs. Our experiments show a strong influence of the objective functions on the success rate. The additional IM implementation improves the results for some objectives. The algorithm with the highest success rate on the more complex benchmark problem employs both, multiple objectives and IM.
A Federated Transfer Learning-Empowered Blockchain-Enabled Secure Knowledge Sharing Scheme for Unmanned Any Vehicles in Smart Cities

Smart cities embrace unmanned autonomous vehicles (UxVs) for urban mobility and addressing challenges. UxVs include UAVs, UGVs, USVs, and UUVs, empowered by AI, particularly deep learning (DL), for autonomous missions. However, traditional DL has limitations in adapting to dynamic environments and raises data privacy concerns. Limited data availability and starting from scratch to adapt to a new environment during missions pose challenges. Additionally, cyber threats, particularly in terms of communication and data security, can jeopardize the missions performed by UxVs. This paper proposes a federated transfer learning scheme for UxVs, sharing prior knowledge and training with limited data while ensuring security through blockchain. Domain adaptation with maximum mean discrepancy enhances the DL model's performance in target domains. The proposed scheme's feasibility is demonstrated in an empirical environment, and it outperforms existing works.
Improvements of Test Variant Assembly Tool for Massive Exams

At the University of Belgrade, School of Electrical Engineering, Programming 1 and Programming 2 are mandatory courses for all first-year students, with approximately 900 students enrolled each year. To streamline the exam preparation and evaluation for the theoretical aspects of these courses, a test with multiple-choice questions is administered. At the Electrical Engineering and Computing study program, where over 700 students take the course every year, these exams are taken through a paper test. This paper examines the limitations of the existing test assembly tool and proposes improvements through the implementation of a new, more flexible tool in Python.
An Ensemble Learning to Detect Decision-Based Adversarial Attacks in Industrial Control Systems

An increasing number of Intrusion Detection Systems (IDSs) rely on Artificial Intelligence (AI), specifically Ma-chine Learning (ML) algorithms, to distinguish between benign and malicious data and detect cyber attacks. However, using ML algorithms exposes IDSs to Adversarial Machine Learning (AML) attacks during the training and test phase. These AML attacks aim to deceive ML algorithms by misclassifying data, posing significant disruptions to the system and its users. Two critical categories of AML attacks are White-box and Black-box attacks, with Black-box attacks being more practical and representative of real-world scenarios. This paper investigates the impact of adversarial examples on supervised ML models in IDSs and proposes an ensemble learning-based detection approach. The study uses a power system dataset and employs Random Forest, AdaBoost, and Decision Tree classifiers to achieve this. During the test phase, adversarial examples are generated using the decision boundary and HopSkipJump attacks, two types of Black-box decision-based attacks. The research applies a deep neural network to the dataset containing the generated adversarial examples to detect these AML attacks, achieving an accuracy of 98 % to 99 %.
Pengaruh Achievement Motivation, Locus of Control, dan Study Habits terhadap Hasil Belajar Matematika Siswa Sekolah Dasar

The study aims to analyze whether achievement motivation, internal locus of control, and study habits influence the learning outcomes of grade 7 students in four regions in Indonesia, namely Sumatera, Java, and Papua. Several experts and previous studies have shown that achievement motivation, locus of control, and study habits can be used as predictors and improve students’ mathematics learning outcomes. This research is a survey with a quantitative approach and was conducted at four schools in the XYZ Foundation. Data collection techniques using questionnaires distributed through Google Form link to measure students' achievement motivations, internal locus of control, and study habits on mathematics learning outcomes of 7th grade students. Data was collected from 183 respondents. Data analysis techniques used descriptive analysis and multiple linear regression with the IBM SPSS 25 application. Based on the result of the data analysis, the regression equation for students' achievement motivation, locus of control, and study habits on mathematics learning outcomes of 7th grade students is . Furthermore, based on the t-test result, achievement motivation with a t count of 3.961, internal locus of control with a t count of -2.994, and study habits with a t count of 1.424 are obtained. The conclusions are achievement motivation and internal locus of control partially have a positive influence and study habits do not affect the learning outcomes of grade 7 mathematics. Based on the value of , as much as 15.9% of independent variables are achievement motivation, internal locus of control, and study habits that simultaneously influence the mathematics learning outcomes of 7th grade students. Theoretical implications and implications of the research are discussed.
Hardware-Based Implementation of Target Tracking in Unmanned Aerial Vehicles (UAVs)

Unmanned Aerial Vehicles (UAVs) have gained sig-nificant attention in various fields, including surveillance, search and rescue, and monitoring applications. One important application for UAV s is target tracking, which requires detecting and tracking a specific object of interest in real time. This paper surveys work done so far in the area of target-tracking in UAV s. It then presents a comprehensive hardware-based framework for target tracking in UAV s. This work utilizes the State-of-the-Art YOLOv8 (You Only Look Once) algorithm for target detection, an efficient high-speed target tracking model, and a Proportional Derivative (PD) control algorithm for precise drone movement control. YOLOv8 provides fast, accurate, and real-time detection of the object of interest, allowing the UAV to detect and identify the target object quickly and reliably. Subsequently, a robust tracking algorithm tracks the identified object across consecutive frames, ensuring accurate localization and trajectory estimation. Furthermore, a PD control algorithm is integrated into the system to enable precise and smooth drone movement. The proposed framework is integrated and used for target tracking in UAV s. Further, this framework is implemented on a UAV. The results demonstrate the effectiveness and robustness of the proposed framework, showcasing its potential for real-world applications.
AutoMESC: Automatic Framework for Mining and Classifying Ethereum Smart Contract Vulnerabilities and Their Fixes

Due to the risks associated with vulnerabilities in smart contracts, their security has gained significant attention in recent years. However, there is a lack of open datasets on smart contract vulnerabilities and their fixes that allows for data-driven research. Towards this end, we propose an automated framework for mining and classifying Ethereum’s smart contract vulnerabilities and their corresponding fixes from GitHub and from the Common Vulnerabilities and Exposures (CVE) records in the National Vulnerability Database. We implemented the proposed method in a fully automated framework, which we call AutoMESC. AutoMESC uses seven of the most well-known smart contract security tools to classify and label the collected vulnerabilities based on vulnerability types. Furthermore, it collects metadata that can be used in data-intensive smart contract security research (e.g., vulnerability detection, vulnerability classification, severity prediction, and automated repair). We used AutoMESC to construct a sample dataset and made it publicly available. Currently, the dataset contains 6.7K smart contract vulnerability-fix pairs written in Solidity. We assess the quality of the constructed dataset in terms of accuracy, provenance, and relevance, and compare it with existing datasets. AutoMESC is designed to collect data continuously and keep the corresponding dataset up-to-date with newly discovered smart contract vulnerabilities and their fixes from GitHub and CVE records.
Enhancing Malicious Phishing Video Detection and Classification Using Sparrow Search Algorithm with Deep Learning Model

The growing significance of short videos on video-sharing sites is accompanied by an escalation in misleading content, causing adverse social concerns. Therefore, it is important to develop and utilize advanced methods for automatically detecting the unreliable videos. These methods must include techniques namely user engagement analysis, metadata, and content, artificial intelligence (AI), fact-checking integration, and community reporting, integrated with efforts for promoting media skills and potentially robust regulatory measures, to mitigate the risky effects of misleading video content. Recent developments of deep learning (DL) models have been found useful in developing malicious video detection models. Consequently, this research presents a sparrow search technique with deep learning-assisted malicious video detection (SSADL-MVD) technique. The goal of the SSADL-MVD technique is to discriminate the input videos into real and malicious ones. To do so, the SSADL-MVD technique initially employs the DenseNet121 model for deriving features from the input videos. This study has employed three types of features for malicious video detection namely features of content features, uploader, and environment. Content features result from video content. Before feature extraction, each audio data changed into text. Additionally, four sorts of content features exist namely counts of personal pronouns, number of modal particles, sentiment polarity, and text length. Besides, the features are provided in long short-term memory (LSTM) system for detection of malicious videos. Finally, the SSA can be applied for optimal hyperparameter selection of LSTM system, which in turn boosts the overall classifier outcomes. The simulation results of the SSADL-MVD approach on malicious video datasets demonstrated its superiority over other DL models.
A Lightweight and Efficient Model for Audio Anti-Spoofing

With the rapid development of speech conversion and speech synthesis algorithms, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. In recent years, researchers had proposed anti-spoofing systems based on hand-crafted features. However, using hand-crafted features rather than raw waveform will lose implicit information for audio anti-spoofing. Inspired by the promising performance of ConvNeXt in classification tasks, we reference the network architecture design of ConvNeXt and propose a Lightweight and Efficient Model for Audio Anti-Spoofing (LEMAAS). With no preceding feature extraction process, we employ raw waveforms as direct inputs to our proposed model. By integrating with the channel attention module and using the focal loss function, the proposed model can focus on the most informative features representation of speech and the difficult samples that are hard to classify. Experimental results show that our proposed system could achieve an equal error rate of 0.64% and min-tDCF of 0.0187 for the ASVspoof 2019 LA evaluation dataset, which outperforms the state-of-the-art systems. Moreover, even when trained only on the ASVspoof 2019 LA dataset, the model still achieved equal error rates of 0.86% and 1.18% on the ASVspoof 2015 development dataset and evaluation dataset, respectively. This demonstrates that our model has achieved promising generalization performance during cross-dataset testing.
Feature Adaptation with CLIP for Few-shot Classification

Large Vision-Language models such as CLIP have demonstrated impressive capabilities in zero-shot recognition. To apply CLIP to few-shot classification tasks, several methods have been proposed based on CLIP, achieving significant improvements. However, these methods either insufficiently leverage CLIP’s prior knowledge during training or neglect the impact of feature adaptation. In this paper, we propose FAR, a novel approach that balances distribution-altered Feature Adaptation with pRior knowledge of CLIP to further improve the performance of CLIP in few-shot classification tasks. Firstly, we introduce an adapter that enhances the effectiveness of CLIP adaptation by amplifying the differences between the fine-tuned CLIP features and the original CLIP features. Secondly, we leverage the prior knowledge of CLIP to mitigate the risk of overfitting. Through this framework, a good trade-off between feature adaptation and preserving prior knowledge is achieved, enabling effective utilization of both components to enhance performance on downstream tasks. We evaluate our method on over 10 datasets for classification, and our approach consistently outperforms existing methods, demonstrating its effectiveness and robustness.
Towards Representation Alignment and Uniformity in Long-tailed Classification

The long-tailed distribution is a commonly observed probability distribution in the real world, wherein a majority of classes possess a large number of samples while a minority of classes have only a few samples. This distribution pattern often leads to imbalanced learning, where the model’s performance becomes dominated by the majority classes and the discriminative ability for minority classes deteriorates. Ideal attributes of representation learning include uniformity and alignment, which entail similar samples being close to each other and the uniform distribution of samples in the feature space to preserve maximal information. While optimizing these attributes directly on balanced datasets yields promising results, no prior efforts have focused on achieving them on long-tailed datasets. Therefore, we propose a novel learning strategy, BalAUM, which addresses this gap by explicitly controlling the optimization of uniformity and alignment, thereby improving the quality of representations. Specifically, we design a balanced alignment and uniformity loss within an AU (Alignment and Uniformity) loss framework. This loss incorporates class weights and class centers to alleviate the bias towards head classes, thus enhancing the optimization of uniformity and alignment for tail classes. Furthermore, considering the scarcity of instances in tail classes, we combine mixup with re-sampling to generate additional samples carrying tail class information, utilizing label re-weighting. This augmentation technique enhances the diversity of tail class samples, thereby improving their uniformity. Experimental results on the CIFAR10-LT, CIFAR100-LT, and ImageNet-LT datasets demonstrate that the BalAUM method achieves competitive performance.
VQ-VDM: Video Diffusion Models with 3D VQGAN

In recent years, deep generative models have achieved impressive performance such as realizing image generation that is indistinguishable from real images. Particularly, Latent Diffusion Models, one of the image generation models, have had a significant impact on society. Therefore, video generation is attracting attention as the next modality. However, video generation is more challenging than image generation due to the consideration of temporal consistency and the increase in computational complexity, since a video is a sequence of multiple frames. In this study, we propose a video generation model based on diffusion models employing 3D VQGAN, which is called VQ-VDM. The proposed model is about nine times faster than the Video Diffusion Models which directly generate videos, since our model generates a latent representation which is decoded into a video by a VQGAN decoder. Moreover, our model can generate higher quality video than prior video generation methods exclude state-of-the-art method.
Targeted Transferable Attack against Deep Hashing Retrieval

With the extensive utilization of deep hashing, there exists a surging interest in studying adversarial attacks against it. Previous methods have demonstrated the superior white-box attack performance against deep hashing. However, the more challenging and realistic targeted black-box attack has not yet been explored sufficiently, which will result in an over-estimation on model robustness. In this paper, we focus on targeted black-box attack based on transferability, and propose a novel Targeted Transferable Attack method against deep hashing with Generative Adversarial Network (TTA-GAN). Specifically, we first propose a new Iterative Anchor code Optimization (IAO) method to generate anchor code with superior representative semantics of target label, which can improve both targeted white-box and black-box performances. Then, we propose a generation-based method to directly generate targeted transferable adversarial example by training a conditional generator and a discriminator. Moreover, to further promote the targeted transferability, we conduct multiple input transformations on the generated adversarial example to alleviate the overfitting phenomenon on source model. Finally, we extend our method to a novel model ensemble attack method TTA-GANens to preserve the representative semantics on multiple models, specialized for deep hashing. Extensive experiments demonstrate the superior targeted black-box attack performance than the state-of-the-art methods.
Personalized Federated Learning via Backbone Self-Distillation

In practical scenarios, federated learning frequently necessitates training personalized models for each client using heterogeneous data. This paper proposes a backbone self-distillation approach to facilitate personalized federated learning. In this approach, each client trains its local model and only sends the backbone weights to the server. These weights are then aggregated to create a global backbone, which is returned to each client for updating. However, the client’s local backbone lacks personalization because of the common representation. To solve this problem, each client further performs backbone self-distillation by using the global backbone as a teacher and transferring knowledge to update the local backbone. This process involves learning two components: the shared backbone for common representation and the private head for local personalization, which enables effective global knowledge transfer. Extensive experiments and comparisons with 12 state-of-the-art approaches demonstrate the effectiveness of our approach.
NeRF-SDP: Efficient Generalizable Neural Radiance Field with Scene Depth Perception

In recent years, neural radiance fields have exhibited impressive performance in novel view synthesis. However, exploiting complex network structures to achieve generalizable NeRF usually results in inefficient rendering. Existing methods for accelerating rendering directly employ simpler inference networks or fewer sampling points, leading to unsatisfactory synthesis quality. To address the challenge of balancing rendering speed and quality in generalizable NeRF, we propose a novel framework, NeRF-SDP, which achieves both efficiency and high fidelity by introducing scene depth perception. We incorporate more scene information into the radiance field by using our proposed geometry feature extraction and depth-encoded ray transformer to improve the model’s inference capabilities with sparse points. With the aid of scene depth perception, NeRF-SDP can better understand the scene’s structure, thus better reconstructing the objects’ edges with significantly fewer artifacts. Experimental results demonstrate that NeRF-SDP achieves comparable synthesis quality to state-of-the-art methods while significantly improving rendering efficiency. Furthermore, ablation studies confirm that the depth-encoded ray transformer enhances the model’s robustness to varying numbers of sampling points.
Achieving Privacy-Preserving Multi-View Consistency with Advanced 3D-Aware Face De-identification

The widespread application of face recognition technology has exacerbated privacy threats. Face de-identification is an effective means of protecting visual privacy by concealing identity information. While deep learning-based methods have greatly improved de-identification results, most existing algorithms rely on 2D generative models that struggle to produce identity-consistent results for multiple views. In this paper, we focus on identity disentanglement within the latest 3D-aware face generation model, and propose an advanced face de-identification framework that can be applied to various scenarios. Our proposed framework disentangles identity from other facial features, modifies only the former and generates the de-identified face using a 3D generator. This approach results in high-quality, identity-consistent de-identification that preserves other facial features. We demonstrate our approach on StyleNeRF, one of the most widely-used style-based neural radiation field models. Through extensive experiments, we demonstrate the effectiveness of our approach in achieving face de-identification both for a single image and group images with the same identity. Our work is a significant step forward in the field of face de-identification, opening up new possibilities for practical applications.