Reviewerly/exHarmony huggingface dataset:
AI Accelerator Design to Optimize Power Consumption for Smart Camera

In today’s era of booming artificial intelligent (AI) applications, smart camera products are widely used in many different practical application environments such as observation, monitoring, recognition and identification. Due to the working characteristics, the smart camera is always used continuously for a long time, increasing the energy consumption. Therefore, smart cameras need a solution to optimize power consumption to ensure system performance while employing artificial intelligence (AI) algorithms in image processing. Designing a smart camera system requires careful attention to this aspect. In this paper, a 2-frequency conversion mode method is proposed to improve the energy efficiency of smart camera systems using AI accelerators. Two test scenarios were conducted covering a series of test cases to demonstrate that the energy consumption of the system is significantly improved compared to previous methods.
Data Augmentation with Large Language Models for Vietnamese Abstractive Text Summarization

Text summarization plays a crucial role in managing the overwhelming volume of information available today. This task aims to condense large amounts of information into summaries. However, the lack of large-scale annotated data in certain languages, such as Vietnamese, poses a substantial challenge for developing effective summarization models. With the recent advancements in large language models, such as GPT-3.5, there is an opportunity to leverage these models to augment data for improving the performance of deep learning models in Vietnamese text summarization. In this paper, we propose an automatic approach that utilizes a large language model to generate additional training examples and to enhance the summarization process for Vietnamese texts.
Unmasking The Artist: Discriminating Human-Drawn And AI-Generated Human Face Art Through Facial Feature Analysis

In the 1990s, the world witnessed a revolutionary breakthrough in the realm of AI-generated art, where its applications surpassed mere visual effects. An ever-increasing number of AI-generating applications emerged, posing a critical challenge for many art contest organizations. These organizations stipulate that the artwork presented must result from human creativity without any help from AI-generated applications. This dilemma accentuates the urgency of drawing a clear distinction between AI-generated and human-made artwork, a task of paramount importance. After observing multiple works of art from AI and humans, the authors notice that the Gradient-Based feature can be utilized to differentiate images generated from different sources of generators. Therefore, the authors decided to create the dataset by collecting paintings from a state-of-the-art AI-generating art model, using algorithms for extracting features with multiple models for detecting AI-generated images and evaluating their performance. Solving this problem can help many art contest organizations as they require artists to create artwork rather than AI.
Analysis of Automated Leukemia Cancer Detection using Feature Selection and Classification Techniques

Leukemia is one of the most dangerous human blood cancers today. Identifying cancer disease in WBC blood microscopic image is complicated as the blood cells contain many dimensions due to high feature evaluation. This research study explores several characteristics of Machine Learning and Deep Learning techniques, focusing on feature selection and classification. The reviews and comparisons show the resultant accuracy of the parameter algorithm in feature selection and classification process by evaluating the testing results in different datasets. Moreover, this study uses other techniques to focus on the problems and identification factors.
Recognition of Handwritten Character using Recognition Model based on SVM

Handwritten character recognition (HCR) has been among the most exciting and demanding study aspects in image analysis and recognition of patterns. It makes a significant contribution to the procedure of autonomous advancement. The OCR-optical character recognition of the files has indeed been compared to handwritten files by a human. This OCR has been used to translate characters from numerous documents like images and word documents. The primary goal of this study is to offer an alternative for varied HCR methods like touch input from a portable display and image folder. It is challenging to train an HCR system under these preconditions effectively. Machine learning (ML) techniques have lately been the primary focus of HCR research, with impressive outcomes. Nevertheless, effective numeral recognition and classification remains a difficult task because of various writing patterns and blurriness, disruption, illumination, and dimension differences in the input collection. Thus, this study offers an effective and efficient HCR system to overcome these constraints, presenting a unique support vector machine (SVM). It has demonstrated quantitative SVM for OCR process performance, which provides a better outcome when modified with an ML technique. The suggested concept was evaluated in a training portion that comprised a variety of elegant characters and digits to learn with greater performance.
Dual-Modality Deep Feature-based Anomaly Detection for Video Surveillance

Detecting anomalies in videos is not only crucial but also an intriguing task in surveillance systems. It is a sequential modeling problem in nature that requires careful selection of spatial and temporal dependent patterns from a sequence of frames. There are several research works from traditional approaches to modern deep learning-based techniques introduced to address this problem. However, there is a huge demand for research and development to ameliorate the performance of the existing solutions. In response to that, this study proposes an improved video anomaly detection model using deep features extracted from a dual-modality input representation. The proposed model demonstrates effectiveness in the benchmark–UCF crime dataset by achieving the best AUC of 87.52%, which is ≈ 12.3% improvement compared to a baseline. The application aspect of this work includes strengthening the security measures in common places, viz. airports, banks, public transits, schools, and shopping complexes by detecting aberrational or suspicious activities in surveillance videos.
AANet: Motorcycle ReID Using Multi-Atrous Convolution and Self-Attention Mechanisms

Recent advancements in ReID systems have primarily focused on individuals and four-wheeled vehicles to meet the rising demand for public safety and traffic management. However, the domain of motorcycle ReID has mainly been overlooked despite its significance in security applications. Extracting features from both motorcycles and their riders poses a unique challenge. In this paper, we present AANet, an innovative deep-learning model designed explicitly for motorcycle ReID. AANet incorporates multi-atrous convolution and self-attention mechanisms to extract local and global features, resulting in a robust embedding for the ReID task. We optimize the loss function and augmentations to address the specific challenges of motorcycle ReID. Our method achieves competitive accuracy and efficiency, surpassing other techniques, with a rank-l accuracy of 86.60 and a mean Average Precision of 88.32 on the Motorcycles ReIdentification dataset, the first extensive motorcycle ReID database collected from urban traffic cameras. The proposed AANet model enhances security measures through improved motorcycle surveillance and identification capabilities.
Efficient Finetuning Large Language Models For Vietnamese Chatbot

Large language models (LLMs), such as GPT-4, PaLM, and LLaMa, have been shown to achieve remarkable performance across a variety of natural language tasks. Recent advancements in instruction tuning bring LLMs with ability in following user’s instructions and producing human-like responses. However, the high costs associated with training and implementing LLMs pose challenges to academic research. Furthermore, the availability of pretrained LLMs and instruction tune datasets for Vietnamese language is limited. To tackle these concerns, we leverage large-scale instruction-following datasets from open-source projects, namely Alpaca, GPT4All, and ChatDoctor, which cover general domain and specific medical domain. To the best of our knowledge, these are the first instructional dataset for Vietnamese. Subsequently, we utilize parameter efficient tuning through Low-Rank Adaptation (LoRA) on two open LLMs: Bloomz (Multilingual) and GPTJ-6B (Vietnamese), resulting four models: Bloomz-Chat, Bloomz-Doctor, GPTJ-Chat, GPTJ-Doctor. Finally, we assess the effectiveness of our methodology on a per-sample basis, taking into consideration the helpfulness, relevance, accuracy, level of detail in their responses. This evaluation process entails the utilization of GPT-4 as an automated scoring mechanism. Despite utilizing a low-cost setup, our method demonstrates about 20-30% improvement over the original models in our evaluation tasks.
Analyzing the Benefits of Data Augmentation for Smart Grid Anomaly Detection and Forecasting

In view of the escalating electricity demand and the pervasive implementation of electrical appliances, the safe and efficient operation of smart grids has been recognized as of significant importance. In recent years, machine learning has been widely applied to smart grid core applications, i.e., anomaly detection and electric load forecasting. In order to achieve precise anomaly detection and accurate time series forecasting, a significant quantity of historical data is usually required for model training. In practice, however, acquiring such a sizable dataset is often accompanied by high costs and many challenges, making it impractical in real-world tasks. In this work, we employ data augmentation techniques to expand the training set size for smart grid anomaly detection and time series forecasting tasks. Specifically, we investigate the efficacy of noise injection and a generative adversarial networks based augmentation method on various machine learning-based models. Extensive experiment results on two real-world datasets demonstrate the effectiveness of data augmentation techniques on anomaly detection and time series forecasting tasks. Experiment results show the benefits of data augmentation and provide guidelines for researchers and engineers.
FedRSMax: An Effective Aggregation Technique for Federated Learning with Medical Images

The traditional deep learning framework faces two critical challenges: limited data available for successful model training and concerns regarding user data privacy. Federated learning, which operates in a decentralised paradigm, offers a promising solution to these challenges. Federated averaging (FedAvg) is a common aggregation procedure in federated contexts. FedAvg, however, experiences convergence issues, especially when there is significant diversity in the data distributions among clients. To address this problem, we explore two effective aggregation techniques, namely random-sampling federated maximum (FedRSMax) and random-sampling federated median (FedRSMed) with adaptive moment estimation (Adam), and compare their performance characteristics with FedAvg. In this study, we use a well-established convolutional neural network (LeNet) as a global model for federated learning, and the HAM10000 dermatoscopic image dataset is used as the primary data source. We balance the dataset and generate random subsets to induce data heterogeneity for different simulated clients and evaluate the performance of the proposed techniques. Our findings demonstrate that FedRSMax outperforms FedRSMed and FedAvg in terms of accuracy, recall, and precision and can therefore serve as an effective alternative for aggregation in federated learning.
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering

Visual Question Answering (VQA) is an intricate and demanding task that integrates natural language processing (NLP) and computer vision (CV), capturing the interest of researchers. The English language, renowned for its wealth of resources, has witnessed notable advancements in both datasets and models designed for VQA. However, there is a lack of models that target specific countries such as Vietnam. To address this limitation, we introduce a transformer-based Vietnamese model named BARTPhoBEiT. This model includes pre-trained Sequence-to-Sequence and bidirectional encoder representation from Image Transformers in Vietnamese and evaluates Vietnamese VQA datasets. Experimental results demonstrate that our proposed model outperforms the strong baseline and improves the state-of-the-art in six metrics: Accuracy, Precision, Recall, Fl-score, WUPS 0.0, and WUPS 0.9.
Evolving Prompts for Synthetic Image Generation with Genetic Algorithm

The growing accessibility of text-to-image generative models necessitates the search for proper prompts to generate preference-satisfying and high-quality images. A prompt generally is a specific text input provided to the generative system to guide the production of corresponding images. However, crafting suitable prompts manually remains a non-trivial challenge. Existing automatic approaches often utilize evolutionary algorithms (EAs) to evolve a population of prompts in a generational manner until suitable prompts that can generate satisfying images can be obtained. Using a genetic algorithm (GA), EvoGen is such a prompt evolving framework with promising image generation capabilities; however, it is still not consistent in yielding desirable results. In this paper, we employ a different GA implementation for EvoGen that has an intrinsic elitism mechanism to ensure that good prompts are not accidentally discarded due to the stochastic operators of EAs. Furthermore, we consider a new cosine loss function as the fitness function that achieves faster convergence and better image guidance.
A Novel Approach for Fall Detection Using Thermal Imaging and a Stacking Ensemble of Autoencoder and 3D-CNN Models

Falls are a significant cause of injury and mortality, particularly among the elderly population. Early detection of falls is crucial to mitigate their impact. Thermal imaging is a promising technology for detecting falls as it is non-invasive and operates in low-light conditions. However, accurately detecting falls in thermal images remains challenging due to the low resolution and lack of color information in these images. This paper proposes a novel approach for improving fall detection in thermal image data using a stacking ensemble of Autoencoder (AE) and 3-D Convolutional Neural Network (3D-CNN) models fed into a meta-neural network which is trained to detect falls and non-falls. The effectiveness of the proposed system is demonstrated through ablation studies on the publicly available benchmark dataset—"Thermal Simulated Fall", in which the model achieves an accuracy of 83%. Comparative analysis shows that the proposed solution outperforms an AE-based baseline by 9.2%. The combination of AEs and 3D-CNNs allows us to harness the power of both supervised and unsupervised learning methodologies, whilst mitigating the limitations and biases of each individual model, offering a promising solution for accurate and efficient fall detection in thermal image input streams.
CompressionGPT: Evaluating Fault Tolerance of a Compressed Large Language Model

Deep neural networks (DNNs) currently require large amounts of memory to store weights. Consequently, inference is less efficient given that weights must be stored off-chip on DRAM, resulting in costly memory accesses. While compression techniques, including quantization and pruning, can significantly reduce model size, current memory technologies are unable to store compressed DNNs on-chip. Prior works have proposed multi-level cell emerging non-volatile memory technologies as a solution given their ability to store bits densely on-chip. While these memory technologies are fault prone, having higher bit error rates, it has been demonstrated that DNNs exhibit some fault tolerance. We build on previous work by examining the fault tolerance of a pruned and quantized large language model (LLM).
Firefly Optimized Federated SVM Model for Breast Cancer Prediction

Cancer is a leading cause of morbidity and mortality worldwide, with an estimation of 10 million deaths attributed to cancer each year. Among various types of cancer, Breast cancer is the most common cancer diagnosed among women, accounting for approximately 30% of all new cancer cases among women. Periodic clinical checks and self-tests will assist in the early identification of Breast cancer. Breast cancer detection at an early stage helps the patients to receive suitable treatment, which can increase the chances of their survivability. Automated clinical solutions, such as Computer-Aided detection and AI-based algorithms, have shown increasing promise in improving the accuracy and efficiency of Breast cancer screening and diagnosis. Several serious challenges have to be addressed such as lack of standardization and regulation of these technologies, the need for large amounts of data for training and validation, as well as patients’ privacy and data security. To address these challenges, a novel Federated Learning architecture that, rather than sharing data, enables knowledge integration by sharing the model parameters of each client during the federated training process. In this paper, we proposed a federated Support Vector Machine for the early detection of Breast cancer. This work utilized the potential benefits of Federated Learning, Firefly algorithm, and Support Vector Machine to study the early detection of Breast cancer disease. The proposed model leverages a distributed computing approach, allowing the SVM model to be trained across multiple clients while preserving data privacy. In addition, we used the Firefly algorithm as a feature selection technique to identify the most relevant features for Breast cancer detection. We evaluated the performance of the proposed model using publicly available Breast cancer datasets namely Wisconsin dataset. The experimental results of this proposed work showed that our proposed model has achieved an accuracy of 95.68%. Finally, we highlighted the significance of the proposed work with the potential benefits of employing Federated Learning in Breast cancer detection.
Masked Face Recognition Using EUM Feature Extraction from Unobstructed Region

Face recognition with masks presents a formidable challenge in the field of computer vision, primarily due to facial occlusion. Facial occlusion occurs when certain facial features are obscured and appear in an image in various sizes and shapes. To address this issue, numerous methods use the entire detected face as input for facial feature extraction; however, the features of the mouth and nostrils are severely degraded, and the number of effective features is drastically reduced. This paper presents a novel method that makes efficient use of information extracted from the unmasked face region using ocular landmarks and the Embedding Unmasking Model (EUM). With 92.38% F1 score, our method attained state-of-the-art (SOTA) performance on the COMASK20.
Attention Pooling for Beta Wavelet Filters in Anomaly Classification with Graph Neural Network

In a standard Graph Neural Network architecture, input node features are aggregated and propagated across the graph. This process usually makes the node features lose their locality. Therefore, the task of anomaly detection becomes less effective due to the over-smoothing phenomenon. The beta wavelet Graph Neural Network (BWGNN) is introduced to address the issue through the usage of beta wavelet filters. In our work, we extend the standard BWGNN by adding an attention-based pooling layer to learn the hidden embedding more efficiently. The attention layer is derived from a multi-head attention network that learns the representation of an input feature sequence of different beta wavelet bases. The proposed architecture is jointly trained with an auxiliary loss which aims to reconstruct the original node features. By testing on two anomaly dataset benchmarks, our proposed architecture can improve the baseline BWGNN significantly and be more stable in classifying graph anomaly nodes.
Connected ResU-Net: A Deep Learning Model for Segmentation of Breast Cancer Ultrasound Images

With Artificial Intelligence (AI)’s advancement in computer vision, AI-aided medical system has become a must, such as for breast cancerous mass detection. Moreover, to fight against this kind of deadly disease, detection in the early stage plays a vital role. A deep learning model can be trained to detect cancerous mass without wasting time. There is widespread agreement that efficient deep network training necessitates thousands of annotated training examples. With this concern, we experimented with the different deep learning models based on U-Net: traditional U-Net, ResU-Net, and Connected U-Net to compare and differentiate between the models for detecting breast cancerous tissue. By analyzing different U-Net variants, a novel U-Net architecture is developed, namely Connected ResU-Net, by combining the algorithm of ResU-Net and Connected U-Net algorithms. With this arrangement, the model is trained for 50 epochs, and to measure the accuracy, the Mean IOU (intersection over union) metric is used, which gives a Mean IOU of 72.50% for the training and 63.10% for the testing dataset.
Decepticon: Attacking Secrets of Transformers

With the growing burden of training deep learning models with huge datasets, transfer learning has been widely adopted (e.g., Transformers like BERT, GPT). Transfer learning significantly reduces the time and effort of model training. However, the security impact of using shared pre-trained models has not been evaluated. In this paper, we provide in-depth characterizations of the fine-tuning process and reveal the security vulnerabilities of transfer-learned models. Then, we show a novel two-level model extraction attack; 1) identifying the pre-trained model of a victim transfer-learned model through model fingerprint collected from off-the-shelf GPUs and 2) extracting the entire weights of the victim black-box model based on the hints in the pre-trained model. The extracted model shows almost alike prediction accuracy with over 94% matching prediction outputs with the victim model. The two-level model extraction enables large model weight extraction that is considered as challenging if not impossible through significantly reduced extraction effort.