In this short note, we formally show that Keyed-Homomorphic Public Key Encryption (KH-PKE) is secure against key recovery attacks and ciphertext validity attacks that have been introduced as chosen-ciphertext attacks for homomorphic encryption.

This paper describes an investigation into the validity of an automatic assessment method of the learner-build concept map by comparing it with two well-known manual methods. We have previously proposed the Kit-Build (KB) concept map framework where a learner builds a concept map by using only a provided set of components, known as the set “kit”. In this framework, instant and automatic assessment of a learner-build concept map has been realized. We call this assessment method the “Kit-Build method” (KB method). The framework and assessment method have already been practically used in classrooms in various schools. As an investigation of the validity of this method, we have conducted an experiment as a case study to compare the assessment results of the method with the assessment results of two other manual assessment methods. In this experiment, 22 university students attended as subjects and four as raters. It was found that the scores of the KB method had a very strong correlation with the scores of the other manual methods. The results of this experiment are one of evidence to show the automatic assessment of the Kit-Build concept map can attain almost the same level of validity as well-known manual assessment methods.

An augmented PAKE (Password-Authenticated Key Exchange) protocol provides password-only authentication in the presence of an attacker, establishment of session keys between the involving parties, and extra protection for server compromise (i.e., exposure of password verification data). Among many augmented PAKE protocols, AMP variants (AMP2 [16] and AMP+ [15]) have been standardized in IEEE 1363.2 [9] and ISO/IEC 11770-4 [10]. In this paper, we thoroughly investigate APKAS-AMP (based on AMP2 [16]) and KAM3 (based on AMP+ [15]) which require several validity checks on the values, received and computed by the parties, when using a secure prime. After showing some attacks on APKAS-AMP and KAM3, we suggest new sanity checks that are clear and sufficient to prevent an attacker from doing these attacks.

This study investigated the test-retest reliability and the criterion-related validity of the Implicit Association Test (IAT [1]) that was developed for measuring shyness among Japanese people. The IAT has been used to measure implicit stereotypes, as well as self-concepts, such as implicit shyness and implicit self-esteem. We administered the shyness IAT and the self-esteem IAT to participants (N = 59) on two occasions over a one-week interval (Time 1 and Time 2) and examined the test-retest reliability by correlating shyness IATs between the two time points. We also assessed the criterion-related validity by calculating the correlation between implicit shyness and implicit self-esteem. The results indicated a sufficient positive correlation coefficient between the scores of implicit shyness over the one-week interval (r = .67, p < .01). Moreover, a strong negative correlation coefficient was indicated between implicit shyness and implicit self-esteem (r = -.72, p < .01). These results confirmed the test-retest reliability and the criterion-related validity of the Japanese version of the shyness IAT, which is indicative of the validity of the test for assessing implicit shyness.

Self-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the clustering validity index.

This letter presents a new inter-cluster proximity index for fuzzy partitions obtained from the fuzzy c-means algorithm. It is defined as the average proximity of all possible pairs of clusters. The proximity of each pair of clusters is determined by the overlap and the separation of the two clusters. The former is quantified by using concepts of Fuzzy Rough sets theory and the latter by computing the distance between cluster centroids. Experimental results indicate the efficiency of the proposed index.

In this paper, conventional validity indexes are reviewed and the shortcomings of the fuzzy cluster validation index based on inter-cluster proximity are examined. Based on these considerations, a new cluster validity index is proposed for fuzzy partitions obtained from the fuzzy c-means algorithm. The proposed validity index is defined as the average value of the relative intersections of all possible pairs of fuzzy clusters in the system. It computes the overlap between two fuzzy clusters by considering the intersection of each data point in the overlap. The optimal number of clusters is obtained by minimizing the validity index with respect to c. Experiments in which the proposed validity index and several conventional validity indexes were applied to well known data sets highlight the superior qualities of the proposed index.

Malignant melanoma is a skin cancer that can be mistaken as a harmless mole in the early stages and is curable only in these early stages. Therefore, dermatologists use a microscope that shows the pigment structures of the skin to classify suspicious skin lesions as malignant or benign. This microscope is called "dermoscope." However, even when using a dermoscope a malignant skin lesion can be mistaken as benign or vice versa. Therefore, it seems desirable to analyze dermoscopic images by computer to classify the skin lesion. Before a dermoscopic image can be classified, it should be segmented into regions of the same color. For this purpose, we propose a segmentation method that automatically determines the number of colors by optimizing a cluster validity index. Cluster validity indices can be used to determine how accurately a partition represents the "natural" clusters of a data set. Therefore, cluster validity indices can also be applied to evaluate how accurately a color image is segmented. First the RGB image is transformed into the L*u*v* color space, in which Euclidean vector distances correspond to differences of visible colors. The pixels of the L*u*v* image are used to train a self-organizing map. After completion of the training a genetic algorithm groups the neurons of the self-organizing map into clusters using fuzzy c-means. The genetic algorithm searches for a partition that optimizes a fuzzy cluster validity index. The image is segmented by assigning each pixel of the L*u*v* image to the nearest neighbor among the cluster centers found by the genetic algorithm. A set of dermoscopic images is segmented using the method proposed in this research and the images are classified based on color statistics and textural features. The results indicate that the method proposed in this research is effective for the segmentation of dermoscopic images.

The explosive growth of World Wide Web usage is causing a number of performance problems, including slow response times, network congestion, and denial of service. Web site that has a huge number of accesses and requires high quality of services, such as a site offering hosting services, or content delivery services, usually uses a cache server to reduce the load on the original server offering the original content. To increase the throughput of the caching process and to improve service availability, multiple cache servers are often positioned in front of the original server. This requires a switch to direct incoming requests to one of the multiple cache servers. In this paper, we propose a routing algorithm for such a switch in front of clustered multiple cache servers and evaluate its performance by simulation. The results show that our routing algorithm is effective when content has request locality and a short period of validity, for example, news, map data, road traffic data, or weather information. We also identify points to consider when the proposed algorithm is applied to a real system.

The structural characteristics of clusters are investigated in the partitioning process. Two partition functions, which show opposite properties around the optimal cluster number, are found and a new cluster validity index is presented based on the combination of these functions. Some properties of the index function are discussed and numerical examples are presented.