Reviewerly/exHarmony huggingface dataset:
Detecting Data Poisoning Attacks using Federated Learning with Deep Neural Networks: An Empirical Study

The advent of intelligent networks powered by machine learning (ML) methods over the past few years has dramatically facilitated various facets of human lives, including healthcare, transportation, and entertainment. However, the use of ML in intelligent networks raises serious concerns about privacy and security, particularly in the context of data poisoning attacks. In order to address these concerns, this research paper presents a novel technique for detecting data poisoning attacks in intelligent networks, focusing on addressing privacy and security concerns associated with the use of machine learning (ML) methods. The research combines federated learning and deep learning approaches to analyze network data in a distributed and privacy-preserving manner. The technique employs a federated neural network to identify malicious data by analyzing network traffic, leveraging the power of Bayesian convolutional neural networks for efficient and accurate detection. The research follows an empirical approach, conducting experimental analyses to evaluate the proposed technique's effectiveness in terms of network security and data classification. The results demonstrate significant performance, including high throughput, quality of service, transmission rate, and low root mean square error for network security. Furthermore, the technique achieves impressive accuracy, recall, precision and malicious data analysis for data detection. The findings of this research contribute to enhancing the security and integrity of intelligent networks, benefiting various stakeholders, including network administrators, data privacy advocates, and users relying on secure network communication.
Thai Finger-Spelling using Vision Transformer

In this paper, we present a finger-spelling recognition system that is based on Thai Sign Language (TFS) and employs a deep learning model called vision transformer. We extracted the 15 characters of the Thai alphabet from publicly available and our collected datasets to establish the recognition system. To train the learning model, we employed four EVA-02 vision transformer models, each of which showed impressive performance across different model sizes. We conducted four experiments to determine the most effective performance model. In Experiment 1, we directly trained the model to compare its performance. In Experiment 2, we used augmentation techniques to generate additional datasets. Experiment 3 utilized the Test-Time Augmentation (TTA) technique to generate test images with random variations. Lastly, in Experiment 4, we used Pseudo-Labelling (labeling labeled and unlabeled data) in each batch to train the model network. Furthermore, we developed a mobile application that collects user image data and provides helpful information related to finger-spelling, such as meanings, gestures, and usage examples.
Developing an Improved Method to Remove Pectoral Muscle for Better Diagnosis of Breast Cancer in Mammography Images

Mammography is a non-invasive method to study breast tissues for abnormalities. Computer-aided diagnosis (CAD) can automate the process of diagnosing malignant and benign tumors accurately. However, accurate results can be hampered by the presence of the pectoral muscle, which has a similar opacity to the breast tissue area. Detecting and removing pectoral muscles is not trivial due to various factors, and there are artifacts present near the pectoral muscle that can hamper proper segmentation. Given the significance of the topic, it is crucial to devise an accurate method for automatically detecting the muscle area in a mammography image and eliminating it from the rest of the image. This process of removing the pectoral muscle from the breast image can aid in precise segmentation and diagnosis of the tumor area, ultimately leading to faster diagnosis and better outcomes for patients. This study examined two segmentation algorithms, Level Set and Region Growing, for segmenting the pectoral muscle. An Improved Region Growing-based (IRG) algorithm was also proposed and showed promising results in automatically segmenting the pectoral muscle. All algorithms were tested on the MIAS dataset, and radiologists evaluated the results, showing an accuracy rating of up to 83% for IRG. The results indicated that IRG outperformed Level Set considerably due to many optimizations and modifications. IRG can be used as part of the preprocessing unit of an automated cancer diagnosis system.
Analyzing Sentiment in Terms of Online Feedback on Top of Users' Experiences

Since most businesses today are conducted online, it is crucial that each customer provide feedback on the various items offered. Evaluating online product sentiment and making suggestions using state-of-the-art machine learning and deep learning algorithms requires a comprehensive pipeline. Thus, this paper addresses the need for a comprehensive pipeline to analyze online product sentiment and recommend products using advanced machine learning and deep learning algorithms. The methodology of the research is divided into two parts: the Sentiment Analysis Approach and the Product Recommendation Approach. The study applies several state-of-the-art algorithms, including Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, Bidirectional Long-Short-Term-Memory (BI-LSTM), Convolutional Neural Network (CNN), Long-Short-Term-Memory (LSTM), and Stacked LSTM, with proper hyperparameter optimization techniques. The study also uses the collaborative filtering approach with the k-Nearest Neighbours (KNN) model to recommend products. Among these models, Random Forest achieved the highest accuracy of 95%, while the LSTM model scored 79%. The proposed model is evaluated using Receiver Operating Characteristic (ROC) - Area under the ROC Curve (AUC). Additionally, the study conducted exploratory data analysis, including Bundle or Bought-Together analysis, point of interest-based analysis, and sentiment analysis on reviews (1996-2018). Overall, the study achieves its objectives and proposes an adaptable solution for real-life scenarios.
Federated-Learning Topic Modeling Based Text Classification Regarding Hate Speech During COVID-19 Pandemic

One of the most challenging tasks in knowledge discovery is extracting the semantics of the content regarding emotional context from the natural language text. The COVID-19 pandemic gave rise to many serious concerns and has led to several controversies including spreading of false news and hate speech. This paper particularly focuses on Islamophobia during the COVID-19. The widespread usage of social media platforms during the pandemic for spreading of false information about Muslims and their common religious practices has further fueled the existing problem of Islamophobia. In this respect, it becomes very important to distinguish between the genuine information and the Islamophobia related false information. Accordingly, the proposed technique in this paper extracts features from the textual content using approaches like Word2Vec and Global Vectors. Next, the text classification is performed using various machine learning and deep learning techniques. The performance comparison of various algorithms has also been reported. After experimental evaluation, it was found that the performance metric like F1-score indicate that Support Vector Machine performs better than other alternatives. Similarly, Convolutional Neural Network also achieved promising results.
Breast Cancer Detection System using Deep Learning Based on Fusion Features and Statistical Operations

Breast cancer is considered as the second cause of death for women. The earlier is diagnosed, the easier the patients can be recovered. The need for studies to detect this kind of cancer easily and accurately came from the growing rate of infected patents by breast cancer exponentially. This study is conducted to investigate the use of deep-learning model for breast cancer detecting using the technique VGG-19 and ultrasound images. Two layers of VGG19 structure were used: (i.e. fc6 and fc7. Based on these two layers (fc6 and fc7), new datasets were created, which are named as statistical operations. These datasets will be employed as input for the following Machine Learning classifiers: K-Nearest Neighbors, Random Forest, Naïve Bayes and Decision Tree. Data augmentation was considered to increase the dataset size for better learning of CNN. Random Forest achieved high accuracy (88.63), precision (0.88), recall (0.88) and F-Measure (0.88). The results of the classification accuracy in the three scenarios are slightly similar; this proves that the breast cancer can be detected even if the size of data in the training dataset was minimal.
Adaptive Gray Wolf Optimization Algorithm based on Gompertz Inertia Weight Strategy

To solve the problems that the Gray Wolf Optimizer (GWO) convergence speed is not fast enough and the solution accuracy is not high enough, this paper proposes an Adaptive Gray Wolf Optimizer based on Gompertz inertia weighting strategy (GGWO). GGWO uses the characteristics of the Gompertz function to achieve nonlinear adjustment of the inertia weight, which better balances the speed of global search and accuracy of local search of the GWO algorithm. At the same time, the Gompertz function is used to realize the adaptive adjustment of the individual gray wolf’s position and to better update the gray wolves’ position according to the fitness values of different gray wolf individuals. Use 6 classic test functions to compare the performance of GGWO in optimization and 10 other classic or improved swarm intelligence algorithms. Results show that GGWO has better solution accuracy, stability, and faster convergence than all other 10 swarm intelligence algorithms.
Basketball Motion Recognition Model Analysis Based on Perspective Invariant Geometric Features in Skeleton Data Extraction

The study proposes a recognition method based on skeleton data to address the basketball action recognition, especially those posed by viewpoint changes in videos. The key of this method is to extract geometric features of viewpoint invariance and combine them with spatio-temporal feature fusion techniques. In addition, the study constructs a dynamic topological map of the human skeleton based on long and short-term neural networks to improve the model performance. The experimental results showed that the research method had an average accuracy of 97.85% for Top-5 metrics on the Kinetics dataset and 97.82% for Top-5 metrics on the NTU RGB+D dataset. It is significantly better than the other three state-of-the-art methods. According to the experimental results, it achieves efficient and stable basketball action recognition, which is significantly superior to existing methods. This research not only provides a more efficient method for basketball motion recognition, but also provides valuable references for other sports action recognition fields.
An Enhanced Approach for Realizing Robust Security and Isolation in Virtualized Environments

Transitioning into the next generation of supercomputing resources, we’re faced with expanding user bases and diverse workloads, increasing the demand for improved security measures and deeper software compartmentalization. This is especially pertinent for virtualization, a key cloud computing component that’s at risk from attacks due to hypervisors’ integration into privileged OSs and shared use across VMs. In response to these challenges, our paper presents a two- pronged approach: introducing secure computing capabilities into the HPC software stack and proposing SecFortress an enhanced hypervisor design. By porting the Kitten Lightweight Kernel to the ARM64 architecture and integrating it with the Hafnium hypervisor, we substitute the Linux-based resource management infrastructure, reducing overheads. Concurrently, SecFortress employs a nested kernel approach, preventing outerOS from accessing mediator’s memory, and creating a hypervisor box to isolate untrusted VMs’ effects. Our initial results highlight significant performance improvements on small scale ARM-based SOC platforms and enhanced hypervisor security with minimal runtime overhead, establishing a solid foundation for further research in secure, scalable high- performance computing.
Optimization of Unsupervised Neural Machine Translation Based on Syntactic Knowledge Improvement

Unsupervised Neural Machine Translation is a crucial machine translation method that can translate in the absence of a parallel corpus and opens up new avenues for intercultural dialogue. Existing unsupervised neural machine translation models still struggle to deal with intricate grammatical relationships and linguistic structures, which leads to less-than-ideal translation quality. This study combines the Transformer structure and syntactic knowledge to create a new unsupervised neural machine translation model, which enhances the performance of the existing model. The study creates a neural machine translation model based on the Transformer structure first, and then introduces sentence syntactic structure and various syntactic fusion techniques, also known as the Transformer combines grammatical knowledge. The results show that the Transformer combines grammatical knowledge paired with Bi-Long Short-Term Memory proposed in this research has better performance. The accuracy and F1 value of the combined model in the training dataset are as high as 0.97. In addition, the time of the model in real sentence translation is controlled within 2s, and the translation accuracy is above 0.9. In conclusion, the unsupervised neural machine translation model proposed in this study has better performance, and its application to actual translation can achieve better translation results.
Analisis Implementasi Kearsipan pada Kantor Desa Peguyangan Kangin

The Peguyangan Kangin Village Office is one of the agencies that manages the operational activities of the village government. Administrative activities of the village office require archive management. The research objective was to find out how the analysis of the implementation of filings at the Peguyangan Kangin Village Office. Data collection techniques include: interviews with heads and staff of administrative and general affairs. Observation by making direct observations in the field to find out the actual conditions regarding the archives at the Peguyangan Kangin Village Office. The data analysis technique used is descriptive qualitative. The results of the research are that the creation of archives has been carried out well. Archives are stored in a special shelf so that they are easy to find, no special standards are applied. The obstacle is the lack of storage space in the village archive warehouse. The advice that can be given is to destroy archives for archives that have not been used for 5-6 years so that old archives do not accumulate in the village archive warehouse. For the destruction of archives, confirmation should be made at the Denpasar City Archives and Libraries Service regarding the archive destruction policy. The village office should digitize in managing archives so that archives stored can be easily searched.
Multi-Stage Classification Scheme to Optimize Medical Treatments

In most existing machine learning and deep learning settings, classification and regression prediction problems may be described as a process where the model output is based on a single-stage input. In most real-life scenarios achieving the desired medical state for the patient may involve dynamically solving drug prescription problems based on the input data at different stages, where each stage is a logical grouping such as timestep division, ICU stay, etc. Data at a given stage represents a recovery progression and can be fundamentally different from the datasets from the previous and future stages. Although A single model may solve the task, a multi-stage learning procedure may be more suitable. To solve this task, we propose an FNN-driven ensemble-based approach for predicting the medications that the patient should receive at each stage of the recovery process. The final medical discharge location is predicted as a result of sequential predictions of drugs and features. In this work, we combine model ensembling and multi-stage iterative learning for solving an optimal drug prescription generation task as a contribution to the existing literature.
Building a Speaker Diarization System: Lessons from VoxSRC 2023

Speaker diarization is the process of partitioning an audio recording into segments corresponding to individual speakers. In this paper, we present a robust speaker diarization system and describe its architecture. We focus on discussing the key components necessary for building a strong diarization system, such as voice activity detection (VAD), speaker embedding, and clustering. Our system emerged as the winner in the Voxceleb Speaker Recognition Challenge (VoxSRC) 2023, a widely recognized competition for evaluating speaker diarization systems.
Image Quality Improvement of Surveillance Camera Image by Learning Noise Removal Method Using Noise2Noise

It is known that images captured by cameras, including surveillance cameras, are degraded by noise due to the shooting environment. Noisy images cannot identify a person’s face or a car license plate number, making it difficult to identify the culprit, which is the purpose of surveillance cameras. Therefore, it is necessary to perform image denoising. In the field of noise reduction, learning methods are currently in the spotlight. However, it is usually difficult to obtain the noiseless images necessary for learning, and there is a problem that they are not practical. In this paper, we investigate an effective noise elimination technique not only for synthetic noise but also for real noise by using a learning method that introduces a technique that does not require clean images. We will also consider improvements to DRUNet [1], which has been shown to be effective as a noise reduction model, in order to make it much lighter and more practical.
SODAWideNet - Salient Object Detection with an Attention Augmented Wide Encoder Decoder Network Without ImageNet Pre-training

Developing a new Salient Object Detection (SOD) model involves selecting an ImageNet pre-trained backbone and creating novel feature refinement modules to use backbone features. However, adding new components to a pre-trained backbone needs retraining the whole network on the ImageNet dataset, which requires significant time. Hence, we explore developing a neural network from scratch directly trained on SOD without ImageNet pre-training. Such a formulation offers full autonomy to design task-specific components. To that end, we propose SODAWideNet, an encoder-decoder-style network for Salient Object Detection. We deviate from the commonly practiced paradigm of narrow and deep convolutional models to a wide and shallow architecture, resulting in a parameter-efficient deep neural network. To achieve a shallower network, we increase the receptive field from the beginning of the network using a combination of dilated convolutions and self-attention. Therefore, we propose Multi Receptive Field Feature Aggregation Module (MRFFAM) that efficiently obtains discriminative features from farther regions at higher resolutions using dilated convolutions. Next, we propose Multi-Scale Attention (MSA), which creates a feature pyramid and efficiently computes attention across multiple resolutions to extract global features from larger feature maps. Finally, we propose two variants, SODAWideNet-S (3.03M) and SODAWideNet (9.03M), that achieve competitive performance against state-of-the-art models on five datasets. We provide the code and pre-computed saliency maps here .
Synthesizing Play-Ready VR Scenes with Natural Language Prompts Through GPT API

In visual computing, 3D scene generation stands as a crucial component, offering applications in various fields such as gaming, virtual reality (VR), and architectural visualization. Creating realistic and versatile virtual environments, however, poses significant challenges. This work presents a novel methodology that leverages the capabilities of a widely adopted large language model (LLM) to address these challenges. Our approach utilizes the GPT API to interpret natural language prompts and generate detailed, VR-ready scenes within Unity3D. Our work is also inherently scalable, since the model accepts any database of 3D objects with minimal prior configuration. The effectiveness of the proposed system is demonstrated through a series of case studies, revealing its potential to generate diverse and functional virtual spaces.
Eigenpatches—Adversarial Patches from Principal Components

Adversarial patches are still a simple yet powerful white-box attack that can be used to fool object detectors by suppressing possible detections. The patches of these so-called evasion attacks are computational expensive to produce and require full access to the attacked detector. This paper addresses the problem of computationally expensiveness by analyzing 375 generated patches, calculating the principal components of these and shows, that traversing the spanned up subspace of the resulting “eigenpatches” can be used to create patches that can be used to fool the attacked YOLOv7 object detector successfully. Furthermore, the influence regarding the mean average precision of the number of principal components used for the patch recreation and the sampling size for the principal component analysis are investigated. Patches generated this way can either be used as a starting point for further optimization or as an adversarial patch as it is.
MANAJEMEN PENGELOLAAN INFORMASI BERBASIS TRANSFORMASI DIGITAL (STUDI KASUS : PENGELOLAAN MEDIA INFORMASI BDK TV) Musyaddad1, Saefudin2

In the era of digital transformation, the amount of data generated is constantly increasing at a rapid pace. Organizations need to be able to manage this data well to make timely decisions and identify new business opportunities. The use of technologies such as data analytics and artificial intelligence can help in effective data management. The purpose of this research is to create and design a breakthrough, especially in digital transformation-based information management through the BDK TV project which will be displayed on a special plate form on a website. This research method uses the content analysis method on the information management of the Palembang Diversity Training Center. The conclusion of this research shows that there is no digital transformation-based information management at the Palembang Religious Education and Training Center. The implications of the research results show the importance of leaders to make policies in changing the pattern of digital transformation-based information management to be carried out in the future.
Edge-Guided Image Inpainting with Transformer

Image inpainting aims to complete missing regions by extracting the features of the image through the information of the known region. Traditional image inpainting approaches like patch-based and diffusion-based methods are robust for simple images, but fail to reconstruct photo-realistic results when the image contains a lot of texture and objects or the object covers a large region in the images. In recent years, transformer-based models are proven to be capable of achieving promising results in numerous image processing tasks. Transformer-based models can better cope with image long-range dependencies which are important for image inpainting. In this paper, we propose a transformer-based generative adversarial network for edge-guided image inpainting. We present the two-step image inpainting approach that first generates edge information and then completes the image. Our experiments demonstrate that the proposed network is able to regenerate the missing regions in images smoothly and realistically and produce visually appealing results against the state-of-the-art image inpainting approaches.