Journal of Science and Technique: Section on Information and Communication Technology
Journal of Le Quy Don Technical University
DEVELOPING OBJECT DETECTION ALGORITHM FOR OPTOELECTRONIC SYSTEMS ON SURFACE VESSELS USING DEEP LEARNING MODELS
Automatic detection of surface vessels is an important task in maritime surveillance and security. This paper proposes an improvement to the Ultralytics YOLOv8 model to achieve higher accuracy and faster processing speed when recognizing surface vessels under harsh lighting and weather conditions. The paper intergrates three main techniques: a new C3Plus block, a Position-wise Spatial Attention (PSA) mechanism, and a Convolutional Block Attention Module (CBAM) module to enhance the network’s feature learning ability. Experiments on a diverse ship image dataset show that the improved model provides an increase in mAP of about 3–6% compared to the original YOLOv8 while maintaining a similar processing speed. In particular, in dark or noisy conditions, the CBAM and PSA improvements help reduce missing objects and improve the model’s robustness.
VINEURO: A MULTIMODAL EEG-BLOOD FUSION MODEL FOR ALZHEIMER’S RISK PREDICTION
Early prediction of Alzheimer’s disease risk is crucial for timely intervention but remains challenging in routine clinical practice. Electroencephalography (EEG) is inexpensive and non-invasive, yet EEG alone often lacks sufficient sensitivity and robustness for reliable early-stage risk estimation. In parallel, routine blood tests capture peripheral immune, inflammatory, and metabolic changes associated with cognitive decline, suggesting that combining EEG with blood-based biomarkers could yield more informative risk stratification. In this work, ViNeuro, a multimodal EEG–blood model tailored to Alzheimer’s risk prediction, is proposed. A single EEG foundation encoder, termed ViNeuro-EEG, is first pretrained using the dual self-supervised objective of the EEGPT model with the criss-cross backbone and learned positional encoding of the CBraMOD model. Pretraining is conducted on a unified corpus of multi-channel clinical EEG data that includes Vietnamese recordings from 108 Military Central Hospital and international datasets. On top of this encoder, a multimodal extension, ViNeuro-MM, is constructed by projecting routine blood biomarkers into the EEG embedding space and using them as queries in a cross-attention layer over EEG tokens. The proposed framework is evaluated on the PEARL-Neuro cohort for Alzheimer’s risk prediction. Compared to its EEG-only counterpart, ViNeuro-MM achieves substantial performance gains, with relative improvements of up to 24.72% in balanced accuracy, demonstrating that fusing routine blood-based biomarkers with EEG foundation representations can markedly enhance early Alzheimer’s risk prediction.
AMCF-NET: ADAPTIVE MULTI-SCALE CROSS-MODAL FUSION NETWORK FOR UAV-SATELLITE CROSS-VIEW LOCALIZATION
Cross-view localization between Unmanned Aerial Vehicle (UAV) and satellite imagery is crucial for autonomous navigation in GPS-denied environments. However, large domain gaps, including viewpoint discrepancies, scale variations, and appearance differences — pose significant challenges. In this paper, we propose the Adaptive Multi-scale Cross-modal Fusion Network (AMCF-Net), a novel approach that effectively addresses these limitations through a shared backbone architecture and adaptive fusion mechanisms. Unlike previous dual-backbone approaches that process UAV and satellite images separately, our method employs a unified FocalNet-Tiny backbone to extract cross-modal features, followed by a Spatially-adaptive Crossmodal Feature Fusion (AMCF) module that dynamically combines multi-scale similarities using learned adaptive weights. This shared representation learning enables better cross-modal alignment and significantly reduces computational overhead. Comprehensive experiments on the UL14 benchmark demonstrate that AMCF-Net achieves state-of-the-art performance, with a Relative Distance Score (RDS) of 78.12% and meter-level accuracy of 27.25% at 3 m, 50.16% at 5 m, 84.37% at 10 m, and finally 88.51% at 20 m. Ablation studies further validate the effectiveness of the shared backbone and adaptive fusion mechanism, demonstrating significant improvements over traditional separate processing approaches.
ENHANCING ADVERSARIAL ROBUSTNESS IN MACHINE LEARNING-BASED MALWARE DETECTION VIA ACTIVATION FUNCTION DESIGN
In recent years, machine learning (ML) has significantly enhanced the efficiency of malware detection systems. Despite achieving high performance, these models now face a growing threat from adversarial attacks. Adversarial malware samples can be intricately crafted to deceive detection models, resulting in misclassifications of malicious programs, thereby allowing them to bypass security systems. Various techniques have been developed to generate adversarial malware specifically designed to evade different ML-based detection systems. This threat underscores the urgent need for solutions that enhance the resilience of malware detection models against adversarial attacks. The paper evaluates and proposes an empirical cost-efficient adversarial defense strategy recommendation via activation function design, that does not require computationally intensive methods such as adversarial training, while boosting the inherent resilience of ML-based malware detection models against black-box attacks. Results show that specific combinations, in particular Rectified Linear Unit (ReLU) and Tanh, can significantly boost robustness without additional training or inference setup. This work provides an empirical design aspect for building intrinsically robust ML-based malware detection systems.
ENHANCING COPYRIGHT PROTECTION AND PROVENANCE IN NFTS WITH BLOCKCHAIN-INTEGRATED FREQUENCY-DOMAIN WATERMARKING
The rapid expansion of the Non-Fungible Token (NFT) market has underscored significant challenges in copyright protection and ownership authentication. While blockchain technology ensures the immutability and transparency of token transactions, the off-chain storage of metadata and original content remains a critical vulnerability, exposing NFTs to risks such as data loss, manipulation, and copyright disputes. In response to these challenges, this study proposes a blockchain-integrated watermarking framework that embeds resilient copyright information into digital assets via a general frequency-domain approach. The watermark is stored off-chain within the InterPlanetary File System (IPFS), while its associated Content Identifier (CID) is anchored in a smart contract, ensuring traceability of provenance and verification of authenticity. Comparative experiments with the Least Significant Bit (LSB) method demonstrate the superior robustness of the proposed frequency-domain technique against various attacks, including compression, noise, and image manipulation. The proposed framework significantly enhances copyright protection, facilitates transparent NFT provenance, and provides a scalable foundation for secure digital asset management within blockchain-based ecosystems.
aDisRAE: ADAPTIVE DISCRIMINATIVE REPRESENTATION AUTOENCODER FOR FEW-SHOT CYBERATTACK DETECTION
Due to the scarcity of labeled anomalous data, few-shot learning has emerged as a critical paradigm for detecting novel and rare cyberattacks. The Discriminative Representation Autoencoder (DisRAE) framework learns a latent space where anomalies are pushed away from a central cluster of normal data, but it struggles with advanced attacks that closely mimic benign behavior. These subtle anomalies are often mapped too close to the normal cluster, leading to detection evasion. To address this limitation, this paper proposes the Adaptive Discriminative Representation Autoencoder (aDisRAE). The framework enhances the training objective by incorporating a prior outlier score that quantifies the subtlety of each anomaly. This score guides an adaptive repulsion mechanism, applying a stronger force to anomalies that most resemble normal data, ensuring a more effective separation in the latent space. The experiments evaluate aDisRAE on three public benchmark datasets: NSL-KDD, CIC-IDS2017 and UNSW-NB15. The results show a notable improvement, raising AUC by up to 10% and boosting robustness, especially against evasive attacks.
EMPIRICAL ANALYSIS OF RGB-IR FEATURE FUSION FOR UAV-BASED OBJECT DETECTION
Object detection based on Unmanned Aerial Vehicles (UAVs) plays a crucial role in applications such as surveillance, disaster management, and military operations. However, traditional methods relying solely on visible Red-Green-Blue (RGB) imagery often perform poorly under low-light conditions and occlusions. To overcome these challenges, recent studies have explored the fusion of RGB and infrared (IR) images, leveraging their complementary properties. Among various fusion strategies, feature-level fusion has gained increasing attention due to its flexibility and superior performance compared to pixel-level and decision-level approaches. Despite its potential, the impact of the specific stage within the network where modality-specific features are integrated remains insufficiently investigated. This study focuses on feature-level fusion and conducts a comprehensive empirical analysis within a unified dual-stream detection framework to examine how fusion at different depths-early, middle, and late-affects detection performance. Additionally, we evaluate multi-position fusion schemes by combining features from multiple levels. Experimental results on the DroneVehicle dataset reveal that middle fusion achieves the best balance between detection accuracy and efficiency among single-layer fusion configurations. Furthermore, early-middle multi-position fusion further improves localization precision, albeit with moderate computational overhead. These findings offer practical insights into designing more effective and efficient RGB-IR fusion networks for UAV-based object detection systems.
MACHINE LEARNING-BASED DOSE RECOMMENDATIONS FOR REMOVAL OF RESIDUAL THYROID TISSUE
Thyroid cancer, particularly differentiated types such as papillary and follicular carcinomas, presents a significant health challenge globally and in Vietnam, where surgical removal of the thyroid gland (thyroidectomy) is the primary treatment approach. However, residual thyroid tissue often remains post-surgery, necessitating effective ablation to prevent recurrence and complications. Radioactive iodine therapy using I-131 is the standard method for ablating this residual tissue. The accuracy of I-131 dose estimation plays a crucial role in ensuring therapeutic success and patient safety. Traditional methods rely on general guidelines and physician expertise but may lack the precision necessary for individual patients. While several previous studies have attempted to improve dose estimation using machine learning approaches, they have often overlooked the importance of data preprocessing techniques. This lack of attention to data quality has limited the performance of predictive models. To address this problem, our study expands upon prior work by placing a strong emphasis on data preprocessing, aiming to enhance model accuracy and reliability. We implement a multi-stage framework that processes medical records and identifies key features, using seven traditional machine learning models for prediction. Our results demonstrate that the Decision Tree model outperforms other models, achieving the highest True Positive Rate of 0.995, a low False Positive Rate of 0.001, and exceptional performance in Recall, Precision, and F1-score (0.995, 0.996, and 0.995, respectively). Moreover, we developed decision rules generated by the Decision Tree for dose prediction, which use a dataset of clinical information from Vietnamese patients. This model represents a promising tool for improving radiation therapy delivery, ensuring more accurate, data-driven decisions, and ultimately better patient outcomes.
ADAPTIVE REVERSIBLE DATA HIDING METHOD USING BITMAP CLASSIFICATION STRATEGY FOR AMBTC-COMPRESSED IMAGES
Reversible data hiding (RDH) has attracted significant attention from researchers. Absolute Moment Block Truncation Coding (AMBTC) compressed image is a popular cover medium in RDH schemes due to its simplicity and high compression ratio. Nevertheless, most AMBTC-based RDH schemes produce stego images that cannot be decoded by standard AMBTC decoders because they use reconstructed pixels to embed data. Although some AMBTC-based RDH schemes generate stego images compatible with AMBTC decoders, their embedding capacity (EC) is limited. To address this issue, in this paper, a high-capacity AMBTC-based RDH scheme compatible with standard decoders is proposed. In this scheme, an effective bitmap classification technique is developed and employed to ensure reversibility and enhance the embedding payload. Furthermore, the proposed scheme provides the ability to adaptively balance the trade-off between hiding the payload and the perceptual quality of stego images by utilizing two predefined thresholds. Experimental results demonstrate that the proposed scheme outperforms related approaches in both embedding payload and visual quality.
A NOVEL FRAMEWORK FOR HTTP/2 OVER MPQUIC: DESIGN AND IMPLEMENTATION
The growing demand for high-performance and reliable web communication has driven significant advancements in transport protocols. This paper introduces an innovative framework for deploying Hypertext Transfer Protocol Version 2 (HTTP/2) over Multipath QUIC (MPQUIC), designed to enhance data transmission efficiency in heterogeneous networks. By integrating HTTP/2’s multiplexing and prioritization capabilities with MPQUIC’s ability to utilize multiple network paths concurrently, the framework addresses critical challenges in stream scheduling and protocol compatibility. To validate its practicality, we design and implement several stream schedulers within the framework. Comprehensive experimental evaluations in Mininet-WiFi confirm the framework’s effectiveness and provide comparative insights into scheduler performance. This work lays a strong foundation for the future development of high-performance web browsing over MPQUIC.
AUTOMATIC FEATURE SELECTION FOR STORM SURGE FORECASTING
Accurate prediction of storm surge events is crucial due to their severe nature, with the potential for widespread flooding and destruction. Timely and precise predictions are necessary to facilitate effective evacuation orders and enable prompt emergency responses. Machine learning has consistently demonstrated its superiority over traditional models in storm surge forecasting in terms of both accuracy and timeliness. However, the accuracy of machine learning models in storm surge prediction greatly relies on selecting the appropriate input features for training. To address this issue, this paper proposes a feature selection method that utilizes a GA to determine the optimal set of input features for machine learning models in storm surge prediction. Through experiments conducted on storm surge data from the Tottori coast of Japan and Vietnam, the proposed approach exhibits significant improvements in the accuracy of machine learning models for storm surge prediction.
A NEW TYPE OF POST-QUANTUM DIGITAL SIGNATURE SCHEME
In this paper, the authors propose a new type of post–quantum digital signature scheme based on hard problems, which belongs to the group of unsolvable problems that currently have no solution. Therefore, this type of digital signature scheme can be resistant to quantum attacks based on the quantum algorithm proposed by P. Shor. In addition to quantum resistance, the type of signature scheme proposed here can also be used as pre–quantum digital signature schemes that are widely used in current practical applications.
EXTRACTING SPATIAL-TEMPORAL FEATURES USING DEEP LEARNING IN COOPERATIVE SPECTRUM SENSING
In cognitive radio systems, spectrum sensing (SS) plays a vital role in detecting the presence of the primary user (PU). In this work, a cooperative spectrum sensing (CSS) model based on a graph convolution network and bidirectional long short-term memory (GCNBiLSTM) is proposed. Specifically, the GCN architecture is applied to extract the relationship between the secondary users (SUs). Besides, the BiLSTM architecture learns the temporal correlation of sensing information at SUs. The presence of PU is decided based on spatialtemporal features, which are combined from the outputs of GCN and BiLSTM. The proposed model is evaluated in a scenario of a dynamic channel (i.e., fading channel). Experimental results show that the GCN-BiLSTM model obtains a detection probability (Pd) of 84.5% and an accuracy of 87.25% at a Signal-to-Noise Ratio (SNR) of -14 dB, demonstrating superior performance compared to the baseline models.
A PROPOSED DISTRIBUTED ARCHITECTURE FOR SEARCHING SEMANTICALLY ON A LARGE DATASET OF HACKING NEWS
In this paper, we propose a distributed architecture to support semantic search on largescale datasets of online technology news. The solution combines knowledge graph modeling, natural language processing techniques, and distributed processing on Apache Spark. The paper presents: (1) A distributed architecture that stores a large-scale semantic news dataset using resource description framework model; (2) A pipeline for extracting knowledge from text using natural language processing (NLP) tools such as dependency parsing and named entity recognition; (3) A distributed search engine that uses keyword expansion and graph reasoning to return semantically related results. The experimental results show that the proposed model improves the semantic search capabilities on large-scale data compared to traditional keywordbased search methods.
IMPROVING PERFORMANCE OF VIETNAMESE SPEAKER RECOGNITION USING TRANSFER LEARNING AND ENSEMBLE EMBEDDING
Speaker recognition technology is crucial for identifying or verifying individuals based on their unique vocal characteristics, such as pitch, tone, and speaking style. This technology is widely used to enhance security, improve customer service, support law enforcement, and personalize interactions with smart devices. In recent years, thanks to the application of deep learning techniques, speaker recognition has made significant progress. However, Vietnamese speaker recognition still faces many challenges. This paper presents new strategies that combine transfer learning and ensemble learning to improve the accuracy of Vietnamese speaker recognition. Experimental results on Vietnamese datasets show significant improvements in recognition accuracy. These findings highlight the potential of tailored approaches to advance speaker recognition technology for Vietnamese speakers and expand its applications in this field.
MITIGATING POISONING ATTACKS TO FEDERATED LEARNING IN IoTs ANOMALY DETECTION WITH ATTENTION AGGREGATION
Federated Learning (FL) is a privacy-preserving approach to train deep neural networks across decentralized devices without sharing raw data. Thus, FL has been popularly applied in domains like anomaly detection in Internet of Things (IoTs). However, IoT networks or devices have limited protection capabilities, resulting in the vulnerability of FL to data poisoning attacks. In order to address this challenge, we propose a new robust FL system designed to counter data poisoning attacks. Our approach, named as Federated Learning with Attention Aggregation (FedAA), leverages AutoEncoder (AE) models for local anomaly detection in IoT networks. In FedAA, the global model is aggregated from local models by using a novel aggregation method, named as Attention Aggregation (AA). This method is specifically designed to mitigate the impact of data poisoning attacks, which often lead to high values of the loss functions in the local models. More precisely, the local models with high loss values are assigned lower attention weights when contributing to the global model aggregation, and vice versa. As a result, the proposed AA method enhances the robustness of FedAA against data poisoning attacks. The extensive experiments are conducted on three datasets including N-BaIoT, NSL-KDD, and UNSW, of IoT anomaly detection. The results show that FedAA is more robust than other FL systems in mitigating data poisoning attacks.
A COLLABORATIVE POSSIBILISTIC FUZZY C-MEANS CLUSTERING APPROACH FOR MULTI-DIMENSIONAL DATA ANALYSIS
The rapid development of data acquisition technologies has led to an explosion of data sources. Many traditional data mining techniques and methods have become outdated and are no longer suitable for solving large, high-dimensional data problems. The paper proposes improving the collaborative possibilistic fuzzy clustering algorithm for multi-dimensional data analysis using random projection feature reduction. The random projection feature reduction technique allows for the preservation of relative distances after dimensional reduction, which can help reduce computational complexity while still ensuring the accuracy of the proposed algorithm compared to the algorithm before dimensionality reduction. The proposed algorithm implemented on the collaborative clustering model can help share information about cluster structure at data sites during computation, allowing problems to be performed where data is located on different computers in a network. Experiments performed on two multidimensional datasets downloaded from the UCI Machine Learning Repository library and remote sensing image data show that the proposed method yields significantly better results than some previously proposed methods. These experimental results demonstrate the potential of developing collaborative clustering models, combined with dimensionality reduction techniques, to tackle high-dimensional and distributed large data problems.
CVE VULNERABILITY CLASSIFICATION IN SOURCE CODE BASED ON TOKEN ANALYSIS AND LSTM NETWORKS
As web applications become increasingly widespread, the importance of source code security is growing rapidly. Exposed vulnerabilities present serious risks to both service providers and customers. Various models have been proposed to address this issue, however, most approaches rely on complex graph structures generated from source code or on expert-driven regular expression patterns. This paper introduces a model that utilizes token-based mechanisms combined with deep learning techniques for efficient vulnerability detection in PHP (Hypertext Preprocessor) web applications. By leveraging the PHP tokenization process, we have developed a custom token that merges tokens, supports key PHP features, and optimizes parsing. Using datasets such as the Software Assurance Reference Dataset (SARD) and SQL Injection Labs (SQLI-LABS), this paper demonstrates the training of a deep learning model with enhanced tokens to effectively detect vulnerabilities in the source code.
ANALYSIS OF OUTAGE PROBABILITY AND INTERCEPT PROBABILITY TRADE-OFF FOR SECURE TWO-WAY RELAYING SCHEMES BETWEEN TWO CLUSTERS OF NODES USING FOUNTAIN CODES
This paper presents two secure two-way relaying schemes between two clusters of nodes: i) 3-phase two-way relaying using Digital Network Coding; ii) conventional 4-phase two-way relaying. In the proposed schemes, one node selected from each cluster will use Fountain codes to encode the original data, and then send encoded packets to nodes in the remaining cluster, with the help of a common relay node. Explicit mathematical expressions are derived to evaluate the outage probability (system outage probability) and intercept probability (system intercept probability) of the proposed schemes over the Rayleigh fading channel. Our results show a reliability-security trade-off as well as the impact of system parameters on the network performances.
A COMBINATION OF DEEP LEARNING AND DENSITY METHOD IN ANOMALOUS HUMAN TRAJECTORY DETECTION
Abnormal human trajectories in working places are often associated with problems such as terrorism, violent attacks, and fire. Therefore, detecting anomalous human trajectories can improve safety and security in working areas. In this work, a novel framework of abnormal trajectory detection is proposed based on combining deep learning and density method. In particular, a Long Short-Term Memory-Autoencoder is first applied to learn informative representations of normal trajectories. Then, the density of trajectory representation in the latent space and reconstruction error of trajectory are used to detect anomalies. A novel metric is also proposed to determine the anomaly scores of trajectories. The proposed framework is evaluated using two real trajectory datasets: the MIT Badge and the sCREEN datasets. The experimental results show that our work effectively detects anomalies, achieving a f1-score of 81.08% on the MIT Badge dataset and 89.57% on the sCREEN dataset.
FACIAL RECOGNITION-BASED APPROACH FOR EFFICIENT PERSONAL INFORMATION RETRIEVAL IN CYBERSPACE
The paper proposes an artificial intelligence system that can perform face searches on the Internet. Face search is the search for images or videos that contain a specific person’s face in cyberspace. The proposed system integrates four key components: the Data Collection Module, Data Storage Mechanism, Search Module, and Analysis Module. By integrating advanced deep learning models, the system achieves high precision and reliability in detecting and recognizing faces. Moreover, our system leverages advanced processing technologies such as parallel computing and optimization techniques to speed up the face search process and handle large-scale data. Experimental results demonstrate that the system achieves remarkable accuracy and efficiency in operation.
THE POST-QUANTUM AND PUBLIC-KEY BLOCK CIPHER ALGORITHMS BASED ON C.E. SHANNON’S THEORY OF PERFECT SECRECY
The article proposes a post-quantum and public-key block cipher algorithms constructed based on C.E. Shannon’s theory of perfect secrecy. The post-quantum block cipher algorithm proposed here can resist various types of attacks assisted by quantum computers. In addition to high security, this algorithm also has the ability to authenticate the origin and integrity of the encrypted messages. The public key block cipher algorithms here are developed from the proposed post-quantum block cipher algorithm combined with the Diffie - Hellman protocol, so this algorithm can be used similarly to pre-quantum block cipher algorithms (DES, AES,...) but the establishment of the shared secret key is completely based on the public key infrastructure (PKI).
INVARIANT ZERO-WATERMARKING ALGORITHM IN DWT-DCT DOMAIN USING ROBUST FEATURES MATCHING
In order to protect the copyright of the digital contents, the robust watermarking techniques are employed based on frequency domains or combined frequency domains. Another techniques, called zero-watermarking which does not modify the original image to embed the watermark but create a watermark from its robust features, is a useful technique for resolving the tension between robustness and invisibility. In this paper, we propose a new zero-watermarking algorithm based on discrete wavelet transform and discrete cosine transform domain with significant feature points matching for improving the robustness. In our proposed method, the original image is firstly separated into three components (Y, Cr, Cb), then its Y-component is performed with discrete wavelet transform, afterwards its LL3 is transformed with discrete cosine transform. To generate the master share of the original image, the DCT-based image is binarized. After performing the Arnold transformation on the watermark, the owner share is generated by taking the XOR operation on the scrambled watermark and master share. The robustness of the our proposed algorithm for imaging processes is analyzed, and the results show that the proposed algorithm is robust to common signal processing such as noise, filtering, JPEG compression, geometric attacks such as rotation, scaling, translation and so on.
SHRINKAGE-BASED WEAKLY-SUPERVISED FEATURE LEARNING TO ENHANCE IoT ANOMALY DETECTION
IoT anomaly detection faces challenges due to the rarity of IoT anomalies and the limited availability of labels. Recent weakly-supervised approaches, like Feature Encoding with AutoEncoder and Weakly-supervised Anomaly Detection (FeaWAD) and an improvement on FeaWAD (iFWAD), address this scarcity by constructing detectors from a combination of unlabeled data and a small labeled anomalous set. While effective, these methods lack constraints during the feature learning stage to delineate normal regions from anomalies. Notably, the Shrink AutoEncoder promotes clustering of normal data around the origin while preserving space for anomalies. Drawing inspiration from the Shrink AutoEncoder, the study aims to introduce Shrink iFWAD (called sFWAD), embedding a shrink regularizer into iFWAD. This term compels the feature encoder of sFWAD to learn penalizing normal data that is close to zero, while simultaneously pushing IoT anomalies further away from zero. This process facilitates the anomalous score generator of sFWAD in efficiently identifying IoT anomalies. The proposed method is evaluated against state-of-the-art weakly-supervised techniques and other common anomaly detection methods using the N-BaIoT dataset. Experimental results indicate that sFWAD often surpasses recent weakly-supervised methods as well as the common techniques in IoT anomaly detection performance. For identifying unknown/new IoT anomalies, Missed Detection Rate from sFWAD (0.008) is much lower than those from iFWAD (0.026) and RoSAS (0.015).
TEXTKNOCKOFF: KNOCKOFF NETS FOR STEALING FUNCTIONALITY OF TEXT SENTIMENT MODELS
Most commercial machine learning models today are designed to require significant amounts of time, money, and human effort. Therefore, intrinsic information about the model (such as architecture, hyperparameters, and training data) needs to be kept confidential. These models are referred to as black boxes, and there is an increasing amount of research focused on both attacking and protecting them. Recent publications have often concentrated on the field of computer vision; in contrast, there is still relatively little research on methods for attacking black box models with textual data. This article introduces a research method for extracting the functionality of a black box model in the task of text sentiment analysis. The method has been effectively tested based on random sampling techniques to reconstruct a new model with equivalent functionality to the original model, achieving high accuracy (94.46% compared to 94.92%) and high similarity (96.82%).
DETECTING ANOMALIES IN VIDEOS USING MEMORY-AUGMENTED AUTOENCODER WITH KEY FRAME SELECTION
In this article, we propose a novel method to train a memory-augmented autoencoder in supervised mode by generating pseudo abnormal videos based on key frame selection techniques. Most video anomaly detection methods employ a machine learning model to learn patterns of normal videos. Any video where the patterns significantly deviate from the learnt patterns is considered an anomaly. However, developing an effective machine learning model for video anomaly detection is a challenging task due to the deficiency of anomalies. Specifically, abnormal samples are often much rarer and harder to collect than normal samples. To address this problem, we propose a novel approach using key frame selection techniques to generate pseudo anomalies. The generated pseudo anomalies are then combined with normal data to create the augmented dataset. The memory-augmented autoencoder is then trained on the augmented datasets. The experimental results show that the AUC scores of the proposed solution are higher than those of the base network architecture from 0.20% to 1.31% on three well-known datasets for video anomaly detection.
ENHANCE STEREO VISUAL ODOMETRY PERFORMANCE BY REMOVING UNSTABLE FEATURES
Visual odometry includes two important stages: 1) feature extraction and 2) pose estimation. The performance of visual odometry is dependent on the quality of features including the number of features, the percentage of the correct matching, and the location of detected features. Usually, RANSAC method has been used in pose estimation to remove outlier and select a good set of features that provide higher accuracy. However, in the case the higher wrong matches, the RANSAC seems to be failing. This article proposes the removing unstable feature method by deep learning-based object detection. The proposed method evaluated on the KITTI dataset shows a higher accuracy 6 - 8% compared to the conventional method.
A TYPE OF POST–QUANTUM CRYPTOGRAPHIC ALGORITHM
The article proposes a type of post–quantum cryptographic algorithm based on hash function and One–Time Pad cipher. Due to inheriting a property called “perfect secrecy” of the One-Time Pad cipher, the algorithms constructed according to the method proposed here can resist various types of attacks with the help of quantum computers. In addition to high security, algorithms of this type also have the ability to authenticate the origin and integrity of the encrypted messages. On the other hand, the one-time key for encryption/decryption is established for each individual message while the management and distribution of the shared secret key between the sender/encryptor and receiver/decryptor is performed similar to symmetric – key cryptosystems being applied in practice.
ENHANCING DRUG DISCOVERY THROUGH A META-PATH BASED OVERSAMPLING APPROACH FOR IMBALANCED DATA
This study proposes a new method to improve the efficiency of drug discovery by repurposing existing drugs, aiming to reduce the time and costs associated with traditional drug development processes, which can span 10 to 15 years and cost billions of dollars. Current approaches focus on leveraging heterogeneous data, such as drug-protein and disease-protein interactions, to construct complex networks that link drugs, proteins, and diseases. However, a significant challenge is the imbalance in data, where numerous unconfirmed potential drug-disease interactions (the majority class) outnumber approved drugs (the minority class), severely impacting the predictive performance of machine learning models. Previous attempts to address this issue have shown limited success. This study introduces a novel approach that integrates meta-paths in heterogeneous information networks with data balancing techniques to tackle this imbalance. Experimental results demonstrate that the proposed method enhances model performance and reliability in identifying new relationships between drugs and diseases. This research represents a promising advancement by leveraging network-based strategies and data balancing techniques to facilitate the rediscovery of drug applications, thereby potentially revolutionizing the pharmaceutical industry’s approach to drug development.
DIRECTION OF ARRIVAL ESTIMATION BASED ON SMOOTH MUSIC ALGORITHM COMBINED SPECTRUM SELECTION TECHNIQUES OF NOISE CHARACTERISTICS OF MARINE TARGETS EQUIPPED PROPELLER
This article demonstrates a proposal for a direction of arrival (DOA) estimation algorithm for passive sonar systems based on the combination of the smooth multiple signal classification (Smooth MUSIC) algorithm and frequency bin selection (BS) of the sound of marine propeller-equipped targets, so-called BS-SMUSIC. The harmonics of propeller shaft and blade frequencies are considered useful information for detecting targets and estimating their parameters, including DOA. Low frequency analysis recording (LOFAR) is utilized to implement BS. By analyzing MUSIC-based algorithm together BS, passive DOA estimation algorithms have been significantly improved. Simulation results have been given in both cases: uncorrelated and correlated sources. Outcomes demonstrate that the BS-SMUSIC algorithm has the best performance in comparison to three MUSIC-based algorithms (MUSSIC, Modified MUSIC and Root MUSIC) in all investigation cases: narrow band signals at 5 dB of signal to noise (SNR); wideband signals at -5 dB of SNR. Besides, BS-SMUSIC algorithm also archives the lowest root mean square error (RMSE) in SNR range -5 dB to 20 dB. In the SNR range, the RMSE of BS-SMUSIC algorithm remains stable and is about 0.3◦ at -5 dB of SNR. This performance reflects its ability to ensure stable operation, super resolution and high accuracy of BS-SMUSIC algorithm for the passive sonar systems.