Reviewerly/exHarmony huggingface dataset:
Attention-Based Randomized Ensemble Multi-Agent Q-Learning

Cooperative multi-agent scenarios are prevalent in real-world applications. Optimal coordination of agents requires appropriate task allocation, considering each task's complexity and each agent's capability. This becomes challenging under decentralization and partial observability, as agents must self-allocate tasks using limited state information. We introduce a novel multi-agent environment in which effective sub-task assignment is crucial for high-scoring performance. In addition, we propose a new multi-agent reinforcement learning framework named as attention-based randomized ensemble multi-agent Q-learning, or AREQ for short. This approach integrates a unique network structure using a multi-head attention mechanism, efficiently extracting task-related information from observations. AREQ also incorporates a randomized ensemble method, enhancing sample efficiency. We explore the impact of this attention-based structure and the random ensemble method through an ablation study and show AREQ's superiority compared to existing MARL methods within our proposed environment.
Privacy-preserving continuous learning for MobileSAM via Federated Learning

The latest foundation model is the most notable technology in the field of artificial intelligence. The Segment Anything Model (SAM), which is currently receiving tremendous attention, is one of the foundation models that will bring about a breakthrough in the field of image segmentation. Federated learning is a very suitable learning structure for efficiently learning these foundation models. Therefore, developing federated learning structures for foundation models is one of the important objectives in the field of federated learning in recent years. However, learning the current foundation model through a federated learning structure is extremely difficult. In particular, the complexity of the model is so high that it requires a very high level of computing resources there are many limitations to doing this on local devices. Therefore, we use a foundational model for image segmentation tasks through the recently published MobileSAM. MobileSAM is a lightweight version of the SAM that can also be learned in federated learning structures. In this paper, we propose a federated learning structure with MobileSAM for privacy-preserving continuous learning. Experiments have shown that MobileSAM learned from federated learning has sufficiently available performance.
Efficient Resource Allocation in Cloud Data Centers using the Whale Optimization Algorithm with Adaptive Penalty Function

The efficient allocation of resources is critical for improving the performance of data centers in cloud environments. It is considered an NP-hard problem. Traditional optimization methods for resource allocation are often inadequate due to their complexity, lack of performance guarantees, and lengthy training times. The Whale Optimization Algorithm (WOA) has recently emerged as a well-accepted alternative for solving constraint optimization problems. This article furthers into the potential of WOA to address resource allocation problems in cloud data centers. To overcome the limitations of WOA and constraints optimization, an adaptive penalty function-based method, namely APFWOA, is proposed to minimize the makespan and execution cost. The APFWOA is evaluated on real and synthetic datasets in a simulated cloud infrastructure environment and compared to other contemporary techniques. The findings of this study indicate that the proposed algorithm surpasses alternative methods in minimizing makespan and execution costs.
Reduced-Complexity Approach for Opacity Enforcement Based on Extended Insertion Functions with Limited Insertion Events

Opacity is a security and privacy property that guarantees predefined secret behavior of a cyber-physical system will not be inferred with certainty by a malicious entity. If a system is opacity-violating, extended insertion functions that insert fake symbols can be implemented to enforce opacity. By constricting the ways in which the fake symbols are inserted, this paper addresses opacity enforcement under the framework of finite state automata using extended insertion functions under event insertion constraints and limited insertion events constraints, i.e., only a limited number of symbols in a specified set can be inserted before and after an actual system output. The notion of (k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> ,k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</inf> )-EIC-enforceability is proposed to describe how to enforce opacity via extended insertion functions in the presence of both event insertion constraints and limited insertion events constraints. A necessary and sufficient condition to check (k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> ,k <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</inf> )-EIC-enforceability is derived by constructing an appropriate verifier.
ResNet-Based Camera Model Identification with Adaptive Preprocessing Module and Weight Fusion of Global Information

Identifying camera models used in digital images is an essential component of image forensics, which plays a vital role in preventing image-related crimes. While deep learning techniques, especially Convolutional Neural Networks (CNNs), are currently the most commonly used approach for Camera Model Identification (CMI) due to their high efficiency and accuracy, the performance of the CNN-based method will be adversely affected by the image content due to its structure.In this study, we introduce a novel approach to CMI, which utilizes the Residual Neural Network (ResNet), Adaptive Preprocessing Module (APM), and Weight Fusion of Global Information (WFGI) to overcome this issue. The results of the experiment indicate that our proposed method has achieved an accuracy exceeding 97.5% when tested on a large-scale dataset that consists of 45 different camera models based on the Vision and Dresden database. Furthermore, our approach exhibits robustness under different attack scenarios, thereby highlighting its potential for practical use in image forensics applications.
A Template Attack on Reduction Without Reference Device on Kyber

In July 2022, the National Institute of Standards and Technology (NIST) announced its selection of four algorithms for post-quantum cryptography standardization in advance. Among these algorithms, Kyber was chosen as the only key encapsulation mechanism (KEM). In the Kyber KEM, the modular reduction function is utilized in numerous areas. We have discovered that by modeling controllable modular reduction functions, unknown modular reduction functions can be targeted. And attacks can then be constructed. Henceforth, profiling can be mounted on the target device. In this paper, we present a machine-learning-based key recovery attack on Kyber, without needing a reference device. We have effectively attacked the modular reduction function. Furthermore, this vulnerability that enables the reuse of the same function could be utilized in other attacks.
An Improved Check Digit-based Participant Identification System for Human Biorepositories

Bioresearch data primarily contain private identifiable information. Thus, to ensure personal information protection, anonymization is required throughout the data collection and storage phases, which necessitates significant efforts to prevent errors. This paper proposes a code system ideally suited for replacing personal identification numbers with unique identifiers. The study further explores a check digit algorithm optimized for minimizing identification errors. The participant identification number, which plays a crucial role in the integrated management of bioresearch data, comprises 10 digits, with the last one serving as the check digit for validating the accuracy of the identification number. Among check digit algorithms available for participant identification number systems, the Verhoeff algorithm has demonstrated superior error detection performance compared to other algorithms. Simulations and applications of the coding system confirmed that the proposed method effectively anonymizes personal identification information with improved accuracy. The method is highly applicable to various scenarios involving the collection and storage of bioresearch data.
Obstacle Avoidance in Dynamic Environment using Particle Swarm Optimization and Kalman Filter

This paper presents a novel approach to tackle obstacle avoidance using the Particle Swarm Optimization (PSO) algorithm, a popular metaheuristic algorithm. One of among contributions is the introduction of the inertia rate concept, which progressively increases the emphasis on local and global optima during exploration. By incorporating this method, the performance of the PSO algorithm is significantly enhanced, resulting in improved obstacle avoidance capabilities. Comparative analysis between scenarios with and without the proposed method demonstrates a noteworthy decrease of 0.13 times in the Average collision count. To facilitate practical implementation in real-world environments, the information used in the algorithm, such as obstacle and robot positions, is subject to noise. The study conducts experiments to compare the outcomes when employing a Kalman Filter (KF) to account for this noise and when not using it.
Translate Han-Nom to Vietnamese using Neural Machine Translation Methods

The Han-Nom language, an ancient language of Vietnam, played a significant role in the country’s history. Its script, known as the Nom script, evolved and flourished between the 10th and 20th centuries. Initially employed for recording names of individuals and locations, the Nom script gradually gained popularity and became integrated into the cultural fabric of the nation. However, over time, the usage and knowledge of the Han-Nom language have waned, leading to its gradual obscurity. To address this issue, we present an innovative translation tool in this article, leveraging the power of deep learning techniques. Our tool focuses on translating Han-Nom script to Vietnamese, the contemporary language of Vietnam. For the translation task, we employ two popular deep learning models: the Transformer and Seq2Seq models. The results obtained from our experiments indicate that both the Transformer and Seq2Seq models achieve satisfactory translations, with the Transformer model exhibiting slightly better performance compared to the Seq2Seq model in terms of BLEU scores. While the translations may not yet be flawless, these initial outcomes provide promising prospects for the preservation and understanding of the Han-Nom language using deep learning techniques.
A Scalable Hate Speech Detection System for Vietnamese Social Media using Real-time Big Data Processing and Distributed Deep Learning

In this study, a system to detect hate and offensive social network comments in real-time using big data and distributed deep learning technology is presented. In the offline phase, state-of-the-art deep learning models are trained in a distributed manner using to BigDL library. The trained models are then integrated into the real-time big data processing component powered by Apache Spark, which a big data framework capable of processing a huge amount of comments in real-time. In the online phase, continuous stream of comments from Facebook are crawled and channeled through Kafka to this real-time big data processing component to output hate speech detection results. These results are then are then analyzed, and the statistical data is displayed in a web-app powered by Flask. Therefore, this work not only focuses on the accuracy but also emphasizes the system’s practicality. Thanks to state-of-the-art deep learning models, the system can achieve high accuracy in the hate speech detection. With the deployed big data technology, the system can collect and process huge amounts of Facebook comments and produce statistical results in real-time.
Facial Symmetry Analysis for Cleft Lip Patients from 4D Point Cloud Data

Cleft lip is one of the most common birth defects. Several operations are performed to form a natural lip. A problem with these operations is that the criteria for the facial symmetry are unclear. Based on this background, we propose a method for evaluating the facial symmetry using 4D point cloud data. We evaluate the facial symmetry in two ways. One is based on the temporal changes in the face landmarks. Corresponding points are searched using two local geometric descriptors. The results are approximated to the 3D lines and these slopes are compared to obtain the left-right difference of the movement. The other is based on the center of gravity. The lip part is extracted from the point cloud and divided into subregions. The centroid coordinates are then calculated for each subregion and compared to obtain the left-right difference of the facial structure. The experiment is performed using artificially generated 4D data. As an experimental result, it is shown that our method can find point correspondences with smaller error than comparative methods.
Real-time Detection and Parameter Estimation of UAV/Drone Signals using Adaptive Thresholds

The detection and parameter estimation of Unmanned Aerial Vehicle (UAV) and drone signals have been playing an essential role in passive surveillance systems since it provides inputs for other algorithms to localize, identify and naturalize hostile targets. In this paper, we propose an effective algorithm for reliably detecting UAV/Drone signals and accurately estimating their parameters using adaptive thresholds. The proposed scheme consists of several stages: adaptive noise floor estimation, detection statistics calculation, rising and falling edge detection, Time Difference of Arrival (TDOA) estimation and amplitude estimation. The proposed algorithm is designed in a sequential manner so that it can be implemented in hardware platforms without any difficulty. The proposed solution has been applied to the localization of UAVs/Drones in 3D space using TDOA principles. The proposed method has been shown to outperform classical techniques in all detection, estimation and localization metrics.
Word-Piece Based Natural Language Recognition for Multimodal Object Retrieval

This paper proposes a word-piece based natural language recognition for multimodal object retrieval. Efforts are being made to enable flexible interaction between humans and robots by incorporating image and natural language-related multimodal AI technologies. This includes research on retrieving appropriate images even when natural language does not explicitly include words representing images. However, the lack of clear words in natural language corresponding to images poses challenges in multimodal object retrieval. In this paper, a proposed approach aims to enhance the performance of multimodal object retrieval through word-piece embedding-based natural language recognition (NLR). An experiment was conducted on multimodal object retrieval using word-piece embedding-based NLR. It demonstrated superior performance compared to word embedding. For example, with a data size of 1070, the top-2 retrieval accuracy achieved through word-piece embedding-based NLR was 0.75, while the top-2 retrieval accuracy achieved through word embedding-based NLR was 0.69.
Face Recognition System for Unconstrained Condition

Face recognition systems have become an integral part of various applications, including security, surveillance, and identity verification. These systems predominantly rely on deep learning-based approaches, leveraging Convolutional Neural Networks (CNNs) and facial feature extraction techniques. While such systems have shown promising results under controlled conditions, their performance tends to degrade significantly when facing with challenges like face mask occlusion or hard recognition poses. This poses a significant concern, especially in the context of recent global events where mask wearing has become prevalent. This paper aims to propose a novel face recognition system to handle unconstrained conditions and provide experimental results to validate its effectiveness.To address these limitations, our proposed system incorporates two key advancements. Firstly, we leverage the Deep3DFace approach to reconstruct a detailed 3D face model. By utilizing this model, we can overcome the partial occlusion caused by masks, allowing the system to extract relevant facial features more accurately, thereby improving recognition performance. Secondly, we use DREAM (Deep Residual Equivariant Mapping), a novel technique that focuses on handling hard recognition poses. DREAM utilizes equivariant mappings to capture pose variations effectively, enabling the system to recognize faces regardless of challenging pose configurations. This approach enhances the system’s ability to handle unconstrained conditions and improves recognition accuracy in real-world scenarios.We deployed our proposed system on the DGX-A100 system, a state-of-the-art hardware platform specifically designed for deep learning tasks. We conducted comprehensive experiments using benchmark datasets containing individuals wearing masks and exhibiting challenging poses. The results demonstrate the effectiveness of our approach, showcasing improved recognition accuracy and robustness compared to existing systems.
Breast Cancer Detection Auxiliary System Leveraging Deep Learning and Mixed Reality

According to the World Health Organization (WHO), in 2022, breast cancer is the most diagnosed cancer among women worldwide, irrespective of age. In Taiwan, it is the most prevalent cancer among women and has the fourth-highest mortality rate. Additionally, diagnosing breast cancer often takes a long time searching for symptoms. In this study, we propose utilizing artificial intelligence image analysis methods and mixed reality interfaces to track and compare breast cancer images. We employed a deep learning convolutional neural network to analyze and classify acquired images of benign or malignant cases. Thus, establishing breast cancer tracking and a diagnostic decision support system for physicians to reference would benefit the future medical field. By incorporating mixed reality technology, doctors can save time and reduce labor costs without being constrained by geographic limitations.
Unveiling the Potential and Limitations of Visual Reinforcement Learning: An Experimental Analysis on the Cart-Pole Inverted Pendulum System

Reinforcement learning can handle what traditional control theory can not cope with. In the field of visual reinforcement learning given only a partially observed high-dimensional image state, the agent still can learn a policy to achieve certain tasks. In this paper, we show the potential and limitations of visual reinforcement learning with an experiment on cart-pole balancing tasks both in simulation and semi-real environment systems.
Proposing Hand Gesture Recognition System Using MediaPipe Holistic and LSTM

The paper proposes a method of recognizing and tracking human hand gestures that can be applied to virtual reality applications, medical monitoring, and smart device control. The proposed method estimates hand gestures using the MediaPipe algorithm to track and long short-term memory (LSTM) to classify these gestures. Nine suggested gestures include punching, cutting, swiping right, swiping left, swiping up, swiping down, raising, waving, and clapping hands. The output of the system is to extract the human upper body skeleton and the corresponding labels for the gestures. The results show that the average accuracy of the proposed solution is 93.3% which is capable to be applied in medical monitoring and controlling of electronic devices in smart homes or virtual reality systems.
Evaluate the Training Set: is it necessary? A theorecal Presentation of Y_Measure : New Metric for Evaluating Training Sets for Supervised Classification

All previous work was aimed at evaluating classification algorithms or the entire classification process, it fits with the studied problem in question, but never the representativeness of the Training Set. The Training Set has a great influence on the accuracy of the classification process and the reliability of results and evaluations. We do not doubt about friability and utility of the various measures proposed and applied, we want by this article to reinforce them by another measure of representativeness of the Training Set to allow the expert to better apprehend the results and to optimize the reliability and robustness of the evaluation and in order to better identify gaps either originates from the algorithm or the Training Set. In this article, we will show the need of new Training Set quality evaluation metric. We based on the theoretical criteria for the generation of an optimal Training Set. "Divide for Reign".
Kernel Random Projection Depth for Outlier Detection

This paper proposes an extension of Random Projection Depth (RPD) to cope with multiple modalities and non-convexity on data clouds. In the framework of the proposed method, the RPD is computed in a reproducing kernel Hilbert space. With the help of kernel principal component analysis, we expect that the proposed method can cope with the above multiple modalities and non-convexity. The experimental results demonstrate that the proposed method outperforms RPD and is comparable to other existing detection models on benchmark datasets regarding Area Under the Curves (AUCs) of Receiver Operating Characteristic (ROC).
Developing an OCR Model for Extracting Information from Invoices with Korean Language

Invoices are commercial documents that contain various pieces of information, including the purchased items, time, and total money. Therefore, the extraction of these key information automatically plays an important role. Although there are several methods in detection and extraction information for documents with Latin characters, methods for Korean character are limited. Therefore, in this paper, an Optical Character Recognition (OCR) model using deep learning model combined with some image preprocessing techniques is proposed to automatically extract proper information from the invoices with Korean language. The proposed OCR model is assessed in a large scale dataset of collected invoices and the results show that 87% F1-score can be achieved with negligible time processing.
Safe Teleoperation of the Vehicle Through Combined Speed and Separation Monitoring with Potential Field Approach

Recent applications of autonomous vehicles have introduced teleoperation technology in order to supplement incomplete performance of the autonomous driving. The study focuses on the application of Speed and Separation Monitoring combined with the potential field approach for meeting the safety requirements in teleoperation during collision-free scenarios. To enhance the efficiency of the Speed and Separation Monitoring and potential field approach applications, a continuous adaption of the vehicle's relative velocity to obstacles is proposed in this study. The research proposed an artificial potential field approach to explore the impact of speed separation monitoring on the mobility of teleoperated unmanned ground vehicles and to evaluate an innovative approach for addressing challenges. Through repeated simulation analysis, it is concluded that the vehicle's path and velocity were modified and maintained protective separation distance.