Digital Visual Skills Education for Digital Inclusion of Elder Women in the Community

AbstractThis paper discusses the use of Digital Storytelling workshop practices for the basic digital visual skill education of elderly women. The DST workshop that was run by the authors provided the environment to experiment with the conventional DST workshop practice for developing the digital visual skills of elder community members in a three-day-workshop, which was originally planned as a one-day-city walk experience. The reasons of such a change are discussed in relation to concepts such as gender, age and digital inclusion

On Cropped versus Uncropped Training Sets in Tabular Structure Detection

Automated document processing for tabular information extraction is highly desired in many organizations, from industry to government. Prior works have addressed this problem under table detection and table structure detection tasks. Proposed solutions leveraging deep learning approaches have been giving promising results in these tasks. However, the impact of dataset structures on table structure detection has not been investigated. In this study, we provide a comparison of table structure detection performance with cropped and uncropped datasets. The cropped set consists of only table images that are cropped from documents assuming tables are detected perfectly. The uncropped set consists of regular document images. Experiments show that deep learning models can improve the detection performance by up to 9% in average precision and average recall on the cropped versions. Furthermore, the impact of cropped images is negligible under the Intersection over Union (IoU) values of 50%-70% when compared to the uncropped versions. However, beyond 70% IoU thresholds, cropped datasets provide significantly higher detection performance

Multidomain transformer-based deep learning for early detection of network intrusion

Timely response of Network Intrusion Detection Systems (NIDS) is constrained by the flow generation process which requires accumulation of network packets. This paper introduces Multivariate Time Series (MTS) early detection into NIDS to identify malicious flows prior to their arrival at target systems. With this in mind, we first propose a novel feature extractor, Time Series Network Flow Meter (TS-NFM), that represents network flow as MTS with explainable features, and a new benchmark dataset is created using TS-NFM and the meta-data of CICIDS2017, called SCVIC-TS-2022. Additionally, a new deep learning-based early detection model called Multi-Domain Transformer (MDT) is proposed, which incorporates the frequency domain into Transformer. This work further proposes a Multi-Domain Multi-Head Attention (MD-MHA) mechanism to improve the ability of MDT to extract better features. Based on the experimental results, the proposed methodology improves the earliness of the conventional NIDS (i.e., percentage of packets that are used for classification) by 5x10^4 times and duration-based earliness (i.e., percentage of duration of the classified packets of a flow) by a factor of 60, resulting in a 84.1% macro F1 score (31% higher than Transformer) on SCVIC-TS-2022. Additionally, the proposed MDT outperforms the state-of-the-art early detection methods by 5% and 6% on ECG and Wafer datasets, respectively.Comment: 6 pages, 7 figures, 3 tables, IEEE Global Communications Conference (Globecom) 202

Table Detection for Visually Rich Document Images

Table Detection (TD) is a fundamental task towards visually rich document understanding. Current studies usually formulate the TD problem as an object detection problem, then leverage Intersection over Union (IoU) based metrics to evaluate the model performance and IoU-based loss functions to optimize the model. TD applications usually require the prediction results to cover all the table contents and avoid information loss. However, IoU and IoU-based loss functions cannot directly reflect the degree of information loss for the prediction results. Therefore, we propose to decouple IoU into a ground truth coverage term and a prediction coverage term, in which the former can be used to measure the information loss of the prediction results. Besides, tables in the documents are usually large, sparsely distributed, and have no overlaps because they are designed to summarize essential information to make it easy to read and interpret for human readers. Therefore, in this study, we use SparseR-CNN as the base model, and further improve the model by using Gaussian Noise Augmented Image Size region proposals and many-to-one label assignments. To demonstrate the effectiveness of proposed method and compare with state-of-the-art methods fairly, we conduct experiments and use IoU-based evaluation metrics to evaluate the model performance. The experimental results show that the proposed method can consistently outperform state-of-the-art methods under different IoU-based metric on a variety of datasets. We conduct further experiments to show the superiority of the proposed decoupled IoU for the TD applications by replacing the IoU-based loss functions and evaluation metrics with proposed decoupled IoU counterparts. The experimental results show that our proposed decoupled IoU loss can encourage the model to alleviate information loss

Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach

Due to the characteristics of Information and Communications Technology (ICT) products, the critical information of ICT devices is often summarized in big tabular data shared across supply chains. Therefore, it is critical to automatically interpret tabular structures with the surging amount of electronic assets. To transform the tabular data in electronic documents into a machine-interpretable format and provide layout and semantic information for information extraction and interpretation, we define a Table Structure Recognition (TSR) task and a Table Cell Type Classification (CTC) task. We use a graph to represent complex table structures for the TSR task. Meanwhile, table cells are categorized into three groups based on their functional roles for the CTC task, namely Header, Attribute, and Data. Subsequently, we propose a multi-task model to solve the defined two tasks simultaneously by using the text modal and image modal features. Our experimental results show that our proposed method can outperform state-of-the-art methods on ICDAR2013 and UNLV datasets.Comment: 6 pages, 7 tables, 4 figures, IEEE Global Communications Conference (Globecom), 202

Collaborative Feature Maps of Networks and Hosts for AI-driven Intrusion Detection

Intrusion Detection Systems (IDS) are critical security mechanisms that protect against a wide variety of network threats and malicious behaviors on networks or hosts. As both Network-based IDS (NIDS) or Host-based IDS (HIDS) have been widely investigated, this paper aims to present a Combined Intrusion Detection System (CIDS) that integrates network and host data in order to improve IDS performance. Due to the scarcity of datasets that include both network packet and host data, we present a novel CIDS dataset formation framework that can handle log files from a variety of operating systems and align log entities with network flows. A new CIDS dataset named SCVIC-CIDS-2021 is derived from the meta-data from the well-known benchmark dataset, CIC-IDS-2018 by utilizing the proposed framework. Furthermore, a transformer-based deep learning model named CIDS-Net is proposed that can take network flow and host features as inputs and outperform baseline models that rely on network flow features only. Experimental results to evaluate the proposed CIDS-Net under the SCVIC-CIDS-2021 dataset support the hypothesis for the benefits of combining host and flow features as the proposed CIDS-Net can improve the macro F1 score of baseline solutions by 6.36% (up to 99.89%).Comment: IEEE Global Communications Conference (Globecom), 2022, 6 pages, 3 figures 4 table

Efficient Information Sharing in ICT Supply Chain Social Network via Table Structure Recognition

The global Information and Communications Technology (ICT) supply chain is a complex network consisting of all types of participants. It is often formulated as a Social Network to discuss the supply chain network's relations, properties, and development in supply chain management. Information sharing plays a crucial role in improving the efficiency of the supply chain, and datasheets are the most common data format to describe e-component commodities in the ICT supply chain because of human readability. However, with the surging number of electronic documents, it has been far beyond the capacity of human readers, and it is also challenging to process tabular data automatically because of the complex table structures and heterogeneous layouts. Table Structure Recognition (TSR) aims to represent tables with complex structures in a machine-interpretable format so that the tabular data can be processed automatically. In this paper, we formulate TSR as an object detection problem and propose to generate an intuitive representation of a complex table structure to enable structuring of the tabular data related to the commodities. To cope with border-less and small layouts, we propose a cost-sensitive loss function by considering the detection difficulty of each class. Besides, we propose a novel anchor generation method using the character of tables that columns in a table should share an identical height, and rows in a table should share the same width. We implement our proposed method based on Faster-RCNN and achieve 94.79% on mean Average Precision (AP), and consistently improve more than 1.5% AP for different benchmark models.Comment: Globecom 202

Dynamic decision making for candidate access point selection

Abstract. In this paper, we solve the problem of candidate access point selection in 802.11 networks, when there is more than one access point available to a station. We use the QBSS (quality of service enabled basic service set) Load Element of the new WLAN standard 802.11e as prior information and deploy a decision making algorithm based on reinforcement learning. We show that using reinforcement learning, wireless devices can reach more efficient decisions compared to static methods of decision making which opens the way to a more autonomic communication environment. We also present how the reinforcement learning algorithm reacts to changing situations enabling self adaptation

Prior Knowledge based Advanced Persistent Threats Detection for IoT in a Realistic Benchmark

The number of Internet of Things (IoT) devices being deployed into networks is growing at a phenomenal level, which makes IoT networks more vulnerable in the wireless medium. Advanced Persistent Threat (APT) is malicious to most of the network facilities and the available attack data for training the machine learning-based Intrusion Detection System (IDS) is limited when compared to the normal traffic. Therefore, it is quite challenging to enhance the detection performance in order to mitigate the influence of APT. Therefore, Prior Knowledge Input (PKI) models are proposed and tested using the SCVIC-APT- 2021 dataset. To obtain prior knowledge, the proposed PKI model pre-classifies the original dataset with unsupervised clustering method. Then, the obtained prior knowledge is incorporated into the supervised model to decrease training complexity and assist the supervised model in determining the optimal mapping between the raw data and true labels. The experimental findings indicate that the PKI model outperforms the supervised baseline, with the best macro average F1-score of 81.37%, which is 10.47% higher than the baseline.Comment: IEEE Global Communications Conference (Globecom), 2022, 6 pages, g figures, 6 table