• KIPS Transactions on Computer and Communication Systems
• /
• v.2 no.10
• /
• pp.421-426
• /
• 2013

As a greater amount of traffic have been generated on the Internet, it becomes more important to know the size of each flow. Many research studies have been conducted on the traffic measurement, and mostly they have focused on how to increase the measurement accuracy with a limited amount of memory. In this paper, we propose an explicit flow labeling technique that can be used to find out the names of the top flows and to increase the counting upper bound of the existing scheme. The labeling technique is applied to CSM (Counter Sharing Method), the most recent traffic measurement algorithm, and the performance is evaluated using the CAIDA dataset.

• Journal of Internet Computing and Services
• /
• v.24 no.4
• /
• pp.127-135
• /
• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• Journal of Internet Computing and Services
• /
• v.23 no.3
• /
• pp.35-45
• /
• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• The Journal of Information Technology
• /
• v.4 no.3
• /
• pp.87-100
• /
• 2001

IETF has proposed integrated services model(Int-Serv) and differentiated service(Diff-Serv) to supply IP QoS in Internet[1][2]. Int-Serv model uses state information of each IP flow, so satisfies QoS according to traffic characteristics, but increases the amount of flow state information with increasing flow number. Diff-Serv uses PHP(Per Hop Behaviour) and there are well-defined classes to provide differentiated traffics with different services according to delay and loss sensitivity. Diff-Serv model can provide diverse services in Internet because of having no the state and signal information of each flow. As MPLS uses the packet forwarding technology based on label, it implements the forwarding engine of high performance easily. The MPLS can set up the path having different and variable bandwidth and assign each path to particular CoS (Class of Service). Therefore it is possible to support the Diff-Serv model of well- defined classes that can provide the differentiated traffic with different services according to delay and loss sensitivity in IP QoS models of IETF. In this paper we propose a scheme that can accommodate Diff-Serv model to provide QoS. The system performance has been estimated by scheduling plan according to traffic classes.