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Understanding Video Semantic Structure with Spatiotemporal Graph Random Walk
Hoyeoung Yun, Minseo Kim, Eun-Sol Kim
http://doi.org/10.5626/JOK.2024.51.9.801
Understanding a long video focuses on finding various semantic units present in the video and interpreting complex relationships among them. Conventional approaches utilize models based on CNNs or transformers to encode contextual information for short clips and then consider temporal relationships among them. However, such approaches struggle to capture complex relationships among smaller semantic units within video clips. In this paper, we present video inputs using a spatiotemporal graph with objects as vertices and relative space-time information between objects as edges, to explicitly express relationships among these semantic units. Additionally, we proposed a novel method to represent major semantic units as compositions of smaller units using high-order relationship information obtained by spatiotemporal random walks on the graph. Through experiments on CATER dataset, which involved complex actions of multiple objects, we demonstrated that our approach exhibited effective semantic unit capturing capabilities.
A Trie-based IoT Data Indexing Scheme Utilizing Temporal Prefixes
Jooyoung Yoon, Sunbeom Kwon, Young-Kyoon Suh
http://doi.org/10.5626/JOK.2024.51.3.280
As Internet of Things technology becomes more widespread, various techniques have been proposed to efficiently retrieve large amounts of IoT sensor data. One of the state-of-the-art techniques, ST-Trie, has been shown to have inferior query processing performance compared to other composite indexing techniques for queries with a narrow time range of less than 1 day. To address this concern, this paper proposes TS-Trie, a trie-based indexing technique using temporal prefixes. TS-Trie uses 20-bit temporal information as a prefix to convert three-dimensional spatiotemporal information into 64-bit one-dimensional key values. It also improves the existing search method by building a double-linked list of nodes after 20 bits. By applying the proposed TS-Trie to three real-world IoT datasets, we measured the performance of processing range queries, k-NN, and Top-k queries. As a result, TS-Trie could on average shorten the query time by about 50%, 40%, and 60%, compared to the existing methods, on the three datasets, respectively. With a high compression ratio of 86%, we further confirmed TS-excellent Trie"s space efficiency and showed an average indexing speed that was around four times faster than previous techniques.
Efficient Distributed Training Method Considering the Energy Level of Edge Devices in Solar-powered Edge AIoT Environments
Yeontae Yoo, IKjune Yoon, Dong Kun Noh
http://doi.org/10.5626/JOK.2023.50.8.720
Solar-powered IoT devices periodically harvest energy and therefore can fundamentally solve the energy limitation of battery-based IoT devices. However, a careful energy consumption policy is required due to the variation in the amount of energy harvested. There is a growing interest in the AI-distributed training models that can improve the quality and performance of training by conducting small training at each edge node and sharing the results with neighbors. However, the straggler node problem may occur in such distributed models, significantly decreasing the overall training speed and exponentially reducing the lifespan of the IoT network due to insufficient energy of specific nodes. This study proposes a technique to prevent the occurrence of straggler nodes as much as possible for efficient AI-distributed training in an AIoT environment composed of solar-powered devices. The proposed scheme uses an approximate computing technique that adapts energy consumption by adjusting the accuracy according to each node’s harvested energy while retaining the minimum accuracy required by the application. Among various approximation computing schemes, this study uses a data-level approximation scheme that adjusts the accuracy by adjusting the sampling rate of the sensing data. The experimental results confirm that the proposed scheme reduces the generation of straggler nodes by efficient and balanced use of each node’s harvested energy.
Prediction of Antibiotic Resistance to Ciprofloxacin in Patients with Upper Urinary Tract Infection through Exploratory Data Analysis and Machine Learning
http://doi.org/10.5626/JOK.2023.50.3.263
Emergency medicine physicians use an empirical treatment strategy to select antibiotics before clinically confirming an antibiotic resistance profile for a patient with a urinary tract infection. Empirical treatment is a challenging task in the context of concern for increased antibiotic resistance of urinary tract pathogens in the community. As a single-institution retrospective study, this study proposed a method for predicting antibiotic resistance using a machine learning algorithm for patients diagnosed with upper urinary tract infection in the emergency department. First, we selected significant predictors using statistical test methods and a game theory based SHAP (SHapley Additive exPlanation), respectively. Next, we compared four classifier performances and proposed an algorithm to assist decision-making in empirical treatment by adjusting the prediction probability threshold. As a result, the SVM classifier using predictors selected through SHAP (65% of the total) showed the highest AUROC (0.775) among all conditions used in the experiment. By adjusting the predictive probability threshold in the SVM, we achieved classification accuracy with a specificity that was 3.9 times higher than empirical treatment while preserving the sensitivity of the doctor"s empirical treatment at 98%.
A Digital Forensic Process for Ext4 File System in the Flash Memory of IoT Devices
Junho Jeong, Beomseok Kim, Jinsung Cho
http://doi.org/10.5626/JOK.2021.48.8.865
With the recent rapid advances in digital communication technology, the spread of IoT(Internet of Things) has accelerated and IoT devices can be utilized to investigate crimes and accidents due to the close connection between human society and IoT devices. Accordingly, with the increasing importance of digital forensics, numerous studies have been conducted. However, most digital forensics research proposed only abstract methodologies due to the various types of IoT devices. In addition, binwalk, which is actively used as a firmware analysis tool, does not adequately analyze and extract the ext4 file system. To solve these problems, this paper proposes a proper extraction and analysis method and a practical process that could extract the ext4 file system from the flash memory of IoT devices using the binwalk with the proposed method. This study also verifies the proposed process with DJI Phantom 4 Pro V2.0 drone.
A User-Centric Conversational Service Mashup Model and Engine
http://doi.org/10.5626/JOK.2021.48.5.584
In Internet of Things (IoT) environments, users not only consume services that are provided by IoT devices, but also create their own service mashup applications. Several visual-based approaches have been proposed to support users in creating IoT service mashups. However, as it is not easy for users to understand the visually-represented execution flow of a service mashup, they often find it difficult to create them. This study proposes a conversational service mashup model and an engine, which end-users without programming experience can use to create IoT service mashups through a natural language. The conversational service mashup model comprises four types of keywords to identify user commands. The service mashup engine comprises an interaction manager, a sematic matching module, and a service mashup module. To evaluate the proposed model, we conduct a case study based on a smart home IoT environment scenario. The study results confirm that end-users can easily use the conversational service mashup model and the engine to create required IoT service mashups.
ConvLSTM-Based COVID-19 Outbreak Prediction using Feature Combination of Multivariate Dataset
Yejin Kim, Seokyeon Kim, Yun Jang
http://doi.org/10.5626/JOK.2021.48.4.405
COVID-19 is transmitted through the droplets expelled by infected people. The propagation of splash is affected by space-time. The transmission of infectious diseases depends on the interaction of various factors such as the health status of the infected and the non-infected people and different environmental factors. However, it is difficult to include all information related to the epidemic in the predictive model and understand the relationship between the information. In this research, we propose a method to include the infectious features of COVID-19 in a learning dataset of the deep learning model and understand the effect of the combination of COVID-19 spreading data on the predictive performance of deep learning. Before predicting, the infectious features of COVID-19 are identified and considerations for including the COVID-19 spreading features are defined in the data preprocessing step. In deep learning modeling, a prediction model using ConvLSTM is designed for spatiotemporal prediction. In the process of testing the model, various features related to COVID-19 spread are combined and the effect of the combination on the performance of the model is analyzed. We tested 120 feature combinations with 47 features composed of personal information of confirmed patients and spatial characteristics of the places that they had visited. We used MAPE as an indicator to evaluate performance of the models. In the case of COVID-19 dataset, the MAPE value of the model with combined features was 1.234, and that of the model with not combined features was 2.217.
A Study on the Intelligent Delivery Management System Using UAV-Edge Computing Technology
Chu Myaet Thwal, Minkyung Lee, Choong Seon Hong
http://doi.org/10.5626/JOK.2020.47.12.1208
With the recent advancement of the digital and Internet-of-Things (IoT) technologies, cities globally are rapidly transforming into smart cities. In a parallel to the IoT technology, another technology that has substantially improved in recent years is the Unmanned Aerial Vehicle (UAV) technology, resulting in cheaper, more powerful and reliable UAVs. In this paper, we integrate the aid of the IoT and propose an intelligent delivery management system in coordination with edge computing and UAV technology. The proposed system is an innovative system that facilitates in reducing the operational delay of the delivery services and provides greater and faster facilities to customers. The whole procedure of the delivery process is managed by the edge-based control stations serving as the media between the retailers and UAVs. These stations are distributed across urban areas and are responsible for assigning tasks to the UAVs by performing crucial calculations and communications between the retailers and UAVs. By applying the proposed intelligent delivery scheme in smart city applications, it can be expected to reduce delays in delivery services because of the shortage of manual labor and traffic conditions, thus providing greater and faster facilities to the customers.
A Trie-based Indexing Scheme for Efficient Retrieval of Massive Spatio-Temporal IoT Sensor Data
Hawon Chu, Young-Kyoon Suh, Ryong Lee, Minwoo Park, Rae-Young Jang, Sang-Hwan Lee, Sa-Kwang Song
http://doi.org/10.5626/JOK.2020.47.12.1199
As the Internet-of-Things (IoT) sensors with enhanced communication technology and computing power have been widely utilized in many areas, a great deal of spatio-temporal data has been continuously generated. Thanks to the remarkable advances in storage technology, it is possible to collect such massive data into storage systems for further high-dimensional analysis. That said, it has been very challenging to speedily locate stored IoT data in a reasonable amount of time due to the heavy volume and complex spatial and temporal attributes. To address this concern, we propose a novel scalable indexing scheme, termed ST-Trie, to support the efficient querying of massive spatial-temporal data collected from IoT sensors. The key idea of our scheme is to encode three-dimensional spatiotemporal information into one-dimensional keys in consideration of time and space locality and then organize the keys into a logical trie structure. In our experiments with real datasets, the proposed scheme outperformed composite indexes by an average of up to 92 times in terms of query response time. In particular, we confirmed that ST-Trie scaled much better than the compared indexes with increasing time ranges.
Performance and Energy Comparison of Different BLAS and Neural Network Libraries for Efficient Deep Learning Inference on ARM-based IoT Devices
http://doi.org/10.5626/JOK.2019.46.3.219
Cloud computing is generally used to perform deep learning on IoT devices. However, its application is associated with limitations such as connection instability, energy consumption for communication, and security vulnerabilities. To solve such problems, recent attempts at performing deep learning within IoT devices have occurred. These attempts mainly suggest either lightweight deep learning models or compression techniques concerning IoT devices, but they lack analysis of the effect when it is performed in actual IoT devices. Since each IoT device has different configuration of processing units and supported libraries, it is necessary to analyze various execution environments in each IoT device in order to perform optimized deep learning. In this study, performance and energy of IoT devices with various hardware configurations were measured and analyzed according to the application of the deep learning model, library, and compression technique. It was established that utilizing the appropriate libraries improve both speed and energy efficiency up to 13.3 times and 48.5 times, respectively.
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