• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.503-523
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• 2022

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 - 45 degrees) and left (30 - 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.78-87
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• 2020

With the evolution of the Internet of Things (IoT), a computing paradigm shift from cloud to edge computing is rapidly taking place to effectively manage the rapidly increasing volume of data generated by various IoT devices. Edge computing is computing that occurs at or near the physical location of a user or data source. Placing computing services closer to these locations allows users to benefit from faster and more reliable services, and enterprises can take advantage of the flexibility of hybrid cloud computing. This paper describes the concept and main benefits of edge computing and presents the trends and future prospects for edge computing technology.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3916-3936
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• 2014

Smartphone applications like games, image processing, e-commerce and social networking are gaining exponential growth, with the ubiquity of cellular services. This demands increased computational power and storage from mobile devices with a sufficiently high bandwidth for mobile internet service. But mobile nodes are highly constrained in the processing and storage, along with the battery power, which further restrains their dependability. Adopting the unlimited storage and computing power offered by cloud servers, it is possible to overcome and turn these issues into a favorable opportunity for the growth of mobile cloud computing. As the mobile internet data traffic is predicted to grow at the rate of around 65 percent yearly, even advanced services like 3G and 4G for mobile communication will fail to accommodate such exponential growth of data. On the other hand, developers extend popular applications with high end graphics leading to smart phones, manufactured with multicore processors and graphics processing units making them unaffordable. Therefore, to address the need of resource constrained mobile nodes and bandwidth constrained cellular networks, the computations can be migrated to resourceful servers connected to cloud. The server now acts as a bridge that should enable the participating mobile nodes to offload their computations through Wi-Fi directly to the virtualized server. Our proposed model enables an on-demand service offloading with a decision support system that identifies the capabilities of the client's hardware and software resources in judging the requirements for offloading. Further, the node's location, context and security capabilities are estimated to facilitate adaptive migration.

• The KIPS Transactions:PartC
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• v.19C no.1
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• pp.1-8
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• 2012

Recently, due to the development of broadband, there is a significant increase in utilizing on-demand Saas (Software as a Service) which takes advantage of the technology. Nevertheless, the academic and practical levels of digital forensics have not yet been established in cloud computing environment. In addition, the data of user behavior is not likely to be stored on the local system. The relevant data may be stored across the various remote servers. Therefore, the investigators may encounter some problems in performing digital forensics in cloud computing environment. it is important to analysis History files, Cookie files, Temporary Internet Files, physical memory, etc. in a viewpoint of client, since the SaaS basically uses the web to connects the internet service. In this paper, we propose the method that analysis the usuage trace of the Saas which is the one of the most popular cloud computing services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3858-3874
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• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• Smart Media Journal
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• v.12 no.2
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• pp.42-55
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• 2023

The combination of blockchain (BC), artificial Intelligence (AI), quantum computing (QC), and the Internet of Things (IoT) can potentially transform various industries and domains, including healthcare, logistics, and finance. In this paper, we look at the trends and developments in integrating these emerging technologies and the potential benefits and challenges that come with them. We present a conceptual framework for integrating BC, AI, QC, and IoT and discuss the framework's key characteristics and challenges. We also look at the most recent cutting-edge research and developments in integrating these technologies, as well as the key challenges and opportunities that come with them. Our analysis highlights the potential benefits of integrating the technologies and looks to increased security, privacy, and efficiency to provide insights into the future of these technologies.

• Journal of Information Technology Applications and Management
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• v.15 no.4
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• pp.221-245
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• 2008

Recently, mobile internet technologies are enabling the possibility of radically new computing services which can share information and communicate with others on the move. In this situation, prior studies have attempted to identify of what factors make people use mobile internet. However, prior researches focused on investigating the factors affecting users' adoption to Mobile Internet. In order to offer beneficial materials to mobile internet companies, we investigated important factors to increase mobile users' value from the viewpoints of customer. In particular, this study was progressed how characteristics of mobile contents affect users' intrinsic attributes and how perceived users' network effects affect users' value in the mobile computing environments. To empirically test proposed hypotheses, we have analyzed 285 survey data gathering from mobile internet users and verified hypotheses by using SEM (structural equation modeling). Based on the results of this study, we found that content accessibility and content quality have significant impacts on users' intrinsic attributes such as perceived usefulness and perceived enjoyment. In addition, this study also revealed that perceived usefulness and perceived enjoyment were positively related to users' emotional value. Moreover, this study found that perceived users' network effects could affect users' monetary value by mediating expected benefits. Based on our findings, we can understand that this perspective can provide a deeper understanding of the mobile users' value adoption by bridging the gap between mobile internet studies and actual practice.

• International Commerce and Information Review
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• v.8 no.1
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• pp.3-13
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• 2006

To develop and validate an instrument to measure user satisfaction in Internet shopping environment, the EUCS instrument by Doll and Torkzadeh (1988) was used for this research. The results of the study shows how the main constructs of the model that will eventually interact for the user satisfaction in internet commerce environment. This research will present significant progress towards keeping the End-User Computing Satisfaction instrument relevant and applicable under the Internet shopping environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.943-961
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• 2020

With the advent of IoT technology and Big Data computing, the importance of WSNs (Wireless Sensor Networks) has been on the rise. For energy-efficient and collection-efficient delivery of any sensed data, lots of novel wireless medium access control (MAC) protocols have been proposed and these MAC schemes are the basis of many IoT systems that leads the upcoming fourth industrial revolution. WSNs play a very important role in collecting Big Data from various IoT sensors. Also, due to the limited amount of battery driving the sensors, energy-saving MAC technologies have been recently studied. In addition, as new IoT technologies for Big Data computing emerge to meet different needs, both sensors and sinks need to be mobile. To guarantee stability of WSNs with dynamic topologies as well as frequent physical changes, the existing MAC schemes must be tuned for better adapting to the new WSN environment which includes energy-efficiency and collection-efficiency of sensors, coverage of WSNs and data collecting methods of sinks. To address these issues, in this paper, a self-organization scheme for mobile sensor networks with mobile multiple sinks has been proposed and verified to adapt both mobile sensors and multiple sinks to 3-dimensional group management MAC protocol. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of the various usage cases. Therefore, the proposed self-organization scheme might be adaptable for various computing and networking environments with big data.