Summary The geometric mean is typically used to measure the mean of inflation rate and population fluctuation. It is also used in the description and analysis of singularities and geometric distance spaces. Gradient descent is an integral part of artificial intelligence. In this study, we transform the gradient calculation from conventional quadratic gradient descent algorithms into a root extraction calculation using geometric means. To eliminate the computational complexity of differential operations in gradient calculation and to easily calculate roots using only fundamental arithmetic operations, we introduce the Kai Fang method, the East Asian traditional root extraction method. To do this, we propose a new quadratic gradient descent method based on geometric means and we apply the Kai Fang method with geometric means to create an improved quadratic gradient descent method. The proposed method shows improved computational ease over conventional methods.

Recently, technologies related to autonomous vehicles have been developing rapidly. In line with the advancement in autonomous driving, fully autonomous vehicles that can be driven without human intervention will soon be commercialized. When fully autonomous vehicles appear, various in-vehicle services will change along with automobile culture. Autonomous vehicles may offer services for enjoying multimedia content and entertainment in the vehicle. In this paper, therefore, we propose a method for recommending multimedia to passengers in the vehicle for new services of this kind. We used video-text embedding as a method for recommending multimedia and conducted experiments using movie trailers and plots. Previous studies on video-text embedding have offered new data, such as audio, or proposed new models based on high-complexity deep learning methods. In this paper, however, video-text embedding is performed based on a pretrained model and multiplication operations between features and vectors. The proposed method shows that video-text embedding can be performed without consuming much time or computing power. Similar movies and dissimilar movies can be identified through the proposed videotext embedding. This was verified by measuring genome matrix similarity, and the information about the movies used for the recommendation confirmed that the results were significant.

Recently, Vijayakumaret al.proposed a dual authentication and key management scheme for secure data transmission in vehicular ad-hoc networks. As described by Vijayakumaret al., a dual authentication scheme and the corresponding group key management mechanism are illustrated successively. The authors claimed that the proposed scheme is resistant to replay attack and masquerade attack. However, we find that the proposed scheme given by Vijayakumaret al.is still vulnerable to replay attack, which could be conducted by reusing previously acquired messages. Moreover, this scheme cannot resist masquerade attack toward the system. For the above consideration, in this paper, modifications toward the existing protocol are presented, so as to provide adequate security assurance toward the mentioned attacks.

As a crucial component of Internet‐of‐Thing (IoT), vehicular ad hoc networks (VANETs) have attracted increasing attentions from both academia and industry fields in recent years. With the extensive VANETs deployment in transportation systems of more and more countries, drivers’ driving experience can be drastically improved. In this case, the real‐time road information needs to be disseminated to the correlated vehicles. However, due to inherent wireless communicating characteristics of VANETs, authentication and group key management strategies are indispensable for security assurance. Furthermore, effective road message dissemination mechanism is of significance. In this paper, we address the above problems by developing a certificateless authentication and road message dissemination protocol. In our design, certificateless signature and the relevant feedback mechanism are adopted for authentication and group key distribution. Subsequently, message evaluating and ranking strategy is introduced. Security analysis shows that our protocol achieves desirable security properties. Additionally, performance analysis demonstrates that the proposed protocol is efficient compared with the state of the art.

There are continuous hacking and social issues regarding APT (Advanced Persistent Threat - APT) attacks and a number of antivirus businesses and researchers are making efforts to analyze such APT attacks in order to prevent or cope with APT attacks, some host PC security technologies such as firewalls and intrusion detection systems are used. Therefore, in this study, malignant behavior patterns were extracted by using an API of PE files. Moreover, the FP-Growth Algorithm to extract behavior information generated in the host PC in order to overcome the limitation of the previous signature-based intrusion detection systems. We will utilize this study as fundamental research about a system that extracts malignant behavior patterns within networks and APIs in the future.

Summary During the past several years, many studies have been conducted to determine the expansions (the expanded forms or definitions) of acronyms used in specific texts by using linguistic and syntactical approaches to overcome disambiguation problems. An acronym is an abbreviation formed of the initial components of multiple words. Using only these initial parts can result in huge mistakes when a machine conducts experiments to determine an acronym's meaning in given texts. Finding expansions of acronyms is currently not a major concern. However, identifying how a polysemous acronym is precisely being used in a specific text is difficult. To solve disambiguation problem acronyms have, this paper proposes a method to identify the most appropriate expansions of an acronym by analysing co‐occurrence words found in articles on Wikipedia. Our goal is not to find an acronym's definition or expansion but to identify the most appropriate expansion of a given acronym as it is used in a given text. Through experiments, we demonstrate the proposed method's efficiency and performance by comparing several results. Copyright © 2014 John Wiley & Sons, Ltd.

As a promising mobility protocol, Proxy Mobile IPv6 (PMIPv6) can provide mobility support without the involvement of the mobile nodes (MNs). It depends on the Local Mobility Anchor (LMA) and Mobility Access Gateway (MAG) to emulate the home network for the attached MNs. Based on this design principle, PMIPv6 works well only for small scale networks. However, with the rapid increase of the MNs as well as the huge traffic loads, PMIPv6 as a centralized mobility management protocol in which both the location management and dada forwarding are performed by the LMA, will induce lots of limitations, such as non-optimal routing, single point of failure, scalability and so on. Therefore, a trend to distributed mobility management (DMM) is popular, and several distributed solutions for PMIPv6 have been proposed until now. In this paper, we survey the different schemes of DMM and focus on the related solutions for PMIPv6 by qualitative analysis, and conclude the remaining challenges and open issues for future development.

The most fundamental step in semantic information processing (SIP) is to construct knowledge base (KB) at the human level; that is to the general understanding and conception of human knowledge. WordNet has been built to be the most systematic and as close to the human level and is being applied actively in various works. In one of our previous research, we found that a semantic gap exists between concept pairs of WordNet and those of real world. This paper contains a study on the enrichment method to build a KB. We describe the methods and the results for the automatic enrichment of the semantic relation network. A rule based method using WordNet's glossaries and an inference method using axioms for WordNet relations are applied for the enrichment and an enriched WordNet (E-WordNet) is built as the result. Our experimental results substantiate the usefulness of E-WordNet. An evaluation by comparison with the human level is attempted. Moreover, WSD-SemNet, a new word sense disambiguation (WSD) method in which E-WordNet is applied, is proposed and evaluated by comparing it with the state-of-the-art algorithm.

Real-time systems have to complete the execution of a task within the predetermined time while ensuring that the execution results are logically correct. Such systems require scheduling methods that can adequately distribute the given tasks to a processor. Scheduling methods that all tasks can be executed within a predetermined deadline are called an optimal scheduling. In this paper, we propose a new and simple scheduling algorithm (LSTR: least slack time rate first) as a dynamic-priority algorithm for a multi-processor environment and demonstrate its optimal possibility through various tests.

This article deals with research that automatically constructs a lexical dictionary of unknown words. The lexical dictionary has been usefully applied to various fields for semantic information processing. It has limitations in which it only processes terms defined in the dictionary. Under this circumstance, the concept of “Unknown Word (UW)” is defined. UW, in this research, is considered a word not defined in WordNet. Here is where a new method to construct UW lexical dictionary through inputting various document collections scattered on the web is proposed. We grasp related terms of UW and measure semantic relatedness (similarity) between an UW and a related term(s). The relatedness is obtained by calculating both probabilistic relationship and semantic relationship. This research can extend UW lexical dictionary with an abundant number of UW. It is also possible to prepare a foundation for semantic retrieval by simultaneously using the UW lexical dictionary and WordNet.

Since the latter half of the 1990s, ontology has been the main area of study in the semantic web. Ontology has been actively studied in several areas for a long time. There are many ontological applications and construction methods. In the study of the semantic web, ontologies are built using OWL capabilities. However, it is not suitable for managing spatial relationships. To represent spatial relationships, we try to create new vocabularies based on these two theories. Modeling topological relationships are accomplished using a neighborhood graph that only considers topological relatedness. In addition, we define the new vocabulary to represent the spatial relationships based on spatial description logic. In this article, we focus on multimedia information retrieval. We use the new vocabularies that were defined using these two theories for representing spatial relationships in image and video retrieval. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 679–692, 2006.

One of the most important objectives of data engineering is to deliver high quality data to users. In this paper, we (1) discuss the definition, problems and improvements methods of data qwuality, and (2) propose a modeling methodology for improving the multimedia data quality. And then, we introduce the method for improving the quality of semantic data using the proposed methodology. In this example, we propose to measure and remove the data ambiguities for improving the data quality.

In this paper, we present an efficient term rewriting technique that computes a degree of term to domain relevance. The proposed method resolves the problems in ontology integrated concept search. Those problems are (i) Pre-defined concept classes in ontology are not relevant to users (no proper concept class for a target annotation has not found). (ii) Too many similar concept classes are provided to a user therefore, a user may fail to choose a correct semantic class for a target annotation (ordinary users are not an expert in concept classification). The method uses sense disambiguation task for finding relevant terms for a given domain. Sense disambiguation requires term-to-term similarity measurement and term frequency measurement. For fair modeling of not observed term frequencies, discounting and redistribution model is applied. The proposed method is a compliment to our previous work presented in [13][14]. Robustness of our method is demonstrated through human judgment test that shows our method allows prediction of precise term list (overall 75% of correct prediction) that are relevant to a given domain.

Human color perception is subjective. In addition to RGB or HSV values representing color content, psychological factors, circumstantial factors, environmental factors, and physiological factors play an important role in encapsulating the color content. Typically, only the RGB or HSV values are used in indexing and retrieval of color images. In this paper, we demonstrate the superior retrieval performance of techniques which employ all of the above factors in color retrieval. We first present the variety of factors involved in human color perception and also an evaluation of the existing color indexing methods. Several interesting problems including comparison of images in the color-perceptual domain and retrieval by color affection are illustrated as potential novel image query mechanisms.