• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.4892-4911
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• 2015

Trust as one of important social relations has attracted much attention from researchers in the field of social network-based recommender systems. In trust network-based recommender systems, there exist normally two roles for users, truster and trustee. Most of trust-based methods generally utilize explicit links between truster and trustee to find similar neighbors for recommendation. However, there possibly exist implicit correlations between users, especially for users with the same role (truster or trustee). In this paper, we propose a novel Collaborative Filtering method called CF-TC, which exploits Trust Context to discover implicit correlation between users with the same role for recommendation. In this method, each user is first represented by the same-role users who are co-occurring with the user. Then, similarities between users with the same role are measured based on obtained user representation. Finally, two variants of our method are proposed to fuse these computed similarities into traditional collaborative filtering for rating prediction. Using two publicly available real-world Epinions and Ciao datasets, we conduct comprehensive experiments to compare the performance of our proposed method with some existing benchmark methods. The results show that CF-TC outperforms other baseline methods in terms of RMSE, MAE, and recall.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.5
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• pp.624-631
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• 2012

This paper presents a high-efficiency current mode class-D(CMCD) power amplifier for the 13.56 MHz band using a Guanella's 1:1 transmission-line transformer and filtering circuits at the output network. The second and third s are filtered out in the load network of the class-D amplifier. The implemented CMCD power amplifier exhibited a power gain of 13.4 dB and a high power-added efficiency(PAE) of 84.6 % at an output power of 44.4 dBm using the 13.56 MHz CW input signal. The second and third distortion levels were -50.3 dBc and -46.4 dBc at the same output power level, respectively.

• Journal of KIISE:Information Networking
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• v.29 no.2
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• pp.188-196
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• 2002

As networked virtual environment (NVE) scales in terms of users and network latency, a key aspect to consider is scalability for interactive performance because a large number of objects likely impose heavy burden especially on the network and computational resources. To improve the scalability, various relevance-filtering mechanism have been proposed. However, the existing filtering mechanism do not scale well in terms of interactive performance as the number of users increase and crowds in a specific place. In this paper, we propose a new scalable filtering scheme which reduces the number of messages by dynamically grouping users based on their interests and distance. Within a group, members communicate with each other with high fidelity. However, a representative sends up-to-dated group information of members with low transmission frequency when they are not of immediate interest but are still within the interest area. The representative is elected from members of the group in distributed manner. The proposed scheme enhances the interactive performance scalability of large-scale NVE systems as much as 18% compared with the existing approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1505-1511
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• 2021

Pretrained models with high performance in various tasks within natural language processing have the advantage of learning the linguistic patterns of sentences using large corpus during the training, allowing each token in the input sentence to be represented with appropriate feature vectors. One of the methods of constructing a corpus required for a pre-trained model training is a collection method using web crawler. However, sentences that exist on web may contain unnecessary words in some or all of the sentences because they have various patterns. In this paper, we propose a dataset construction method for filtering sentences containing unnecessary words using neural network models for corpus collected from the web. As a result, we construct a dataset containing a total of 2,330 sentences. We also evaluated the performance of neural network models on the constructed dataset, and the BERT model showed the highest performance with an accuracy of 93.75%.

• The KIPS Transactions:PartB
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• v.18B no.6
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• pp.365-374
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• 2011

Recently, malicious video classification and filtering techniques are of practical interest as ones can easily access to malicious multimedia contents through the Internet, IPTV, online social network, and etc. Considerable research efforts have been made to developing malicious video classification and filtering systems. However, the malicious video classification and filtering is not still being from mature in terms of reliable classification/filtering performance. In particular, the most of conventional approaches have been limited to using only the spatial features (such as a ratio of skin regions and bag of visual words) for the purpose of malicious image classification. Hence, previous approaches have been restricted to achieving acceptable classification and filtering performance. In order to overcome the aforementioned limitation, we propose new malicious video classification framework that takes advantage of using both the spatial and temporal features that are readily extracted from a sequence of video frames. In particular, we develop the effective temporal features based on the motion periodicity feature and temporal correlation. In addition, to exploit the best data fusion approach aiming to combine the spatial and temporal features, the representative data fusion approaches are applied to the proposed framework. To demonstrate the effectiveness of our method, we collect 200 sexual intercourse videos and 200 non-sexual intercourse videos. Experimental results show that the proposed method increases 3.75% (from 92.25% to 96%) for classification of sexual intercourse video in terms of accuracy. Further, based on our experimental results, feature-level fusion approach (for fusing spatial and temporal features) is found to achieve the best classification accuracy.

• Journal of KIISE:Information Networking
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• v.33 no.6
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• pp.407-419
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• 2006

As substituting IPv6 network for all IPv4 network in a short time seems unattainable due to high cost and technical limitation, IPv4 and IPv6 are expected to coexist for a certain period of time. Under the co]existing environment of IPv4 and IPv6, interworking brings a number of extra security considerations even if it may have no security problem for each protocol respectively. Thus, the analysis and solutions for those various attacks toward IPv4/IPv6 interworking-related security are inevitably required for the sake of effective transition and settlement to IPv6. In this paper we carried out a proper rule of packet filtering for IPv6-in-IPv4 tunneling interworking environment to protect the IPv4/IPv6 interworking-related security attacks. Design and implementation of the packet filtering system suitable for IPv4/IPv6 tunneling environment in the form of Linux netfilter and ip6tables are also shown. Thru this study, the packet filtering system was found operating correctly ill the tunneling mechanism.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.77-82
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• 2013

The filtering characteristics of planar distributed feedback (DFB) waveguides composed by gratings with different period are solved using equivalent transmission-line network. To analyze explicitly its band-pass and resonance properties, a longitudinal modal transmission-line theory (L-MTLT) based on Floquet's theorem and Babinet's principle is newly developed. The numerical results reveal that this approach offers a simple and analytic algorithm to analyze the filtering characteristics of cascaded DFB structure with different period, and the bandwidth and side-lobe suppression of cascaded DFB filter are sensitively dependent on the variation of aspect ratio and the number of grating at each region.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.56-64
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• 2015

The personal image information compiled through closed-circuit television (CCTV) will be open to the internet with the technology such as Long-Tail, Mash-Up, Collective Intelligence, Tagging, Open Application Programming Interface (Open-API), Syndication, Podcasting and Asynchronous JavaScript and XML (AJAX). The movie User Created Contents (UCC) connected to the internet with the skill of web 2.0 has the effects of abuse and threat without precedent. The purpose of this research is to develop the institutional and technological method to reduce these effects. As a result of this research, in terms of technology this paper suggests Privacy Zone Masking, IP Filtering, Intrusion-detection System (IDS), Secure Sockets Layer (SSL), public key infrastructure (PKI), Hash and PDF Socket. While in terms of management this paper suggests Privacy Commons and Privacy Zone. Based on CCTV-UCC linked to the above network, the research regarding personal image information security is expected to aid in realizing insight and practical personal image information as a specific device in the following research.

• Journal of Information Technology Applications and Management
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• v.23 no.4
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• pp.55-70
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• 2016

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4271-4294
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• 2018

Communication cost is the most important factor in Wireless Sensor Networks (WSNs), as exchanging control keying messages consumes a large amount of energy from the constituent sensor nodes. Time-based Dynamic Keying and En-Route Filtering (TICK) can reduce the communication costs by utilizing local time values of the en-route nodes to generate one-time dynamic keys that are used to encrypt reports in a manner that further avoids the regular keying or re-keying of messages. Although TICK is more energy efficient, it employs no re-encryption operation strategy that cannot determine whether a healthy report might be considered as malicious if the clock drift between the source node and the forwarding node is too large. Secure SOurce-BAsed Loose Synchronization (SOBAS) employs a selective encryption en-route in which fixed nodes are selected to re-encrypt the data. Therefore, the selection of encryption nodes is non-adaptive, and the dynamic network conditions (i.e., The residual energy of en-route nodes, hop count, and false positive rate) are also not focused in SOBAS. We propose an energy efficient selection of re-encryption nodes based on fuzzy logic. Simulation results indicate that the proposed method achieves better energy conservation at the en-route nodes along the path when compared to TICK and SOBAS.