Online shopping is becoming more and more popular in recent years. However, users are still unable to find what they want on the online market very efficiently. In our previous paper we suggested a system that helps to find short-sleeved T-shirts for young women on the online market. Then we conducted several verification experiments and proved that the system was effective. In this paper, we modified the system by adopting the following schemes in order to make it more versatile; First, all information is presented in a unified format. Second, users are provided with multiple-choice key words. Third, users search results are used to select information that is truly useful for the user. Lastly, we conducted several verification experiments and proved that these schemes were effective.

In this paper, we propose a self-nonself recognition algorithm based on positive and negative selection used in the developing process of T cells. The anomaly detection algorithm based on negative selection is a representative model among self-recognition method and it has been applied to computer immune systems in recent years. In biological immune systems, T cells are produced through both positive and negative selection. Positive selection is the process used to determine a MHC receptor that recognizes self-molecules. Negative selection is the process used to determine an antigen receptor that recognizes antigens, or nonself cells. In this paper, we propose a self-recognition algorithm based on the positive selection and also propose a fusion algorithm based on both positive and negative selection. To verify the effectiveness of the proposed system, we show simulation results for detecting some infected data obtained from cell changes and string changes in the self-file.

Although many researchers have verified the superiority of Support Vector Machine (SVM) on text categorization tasks, some recent papers have reported much lower performance of SVM based text categorization methods when focusing on all types of parts of speech (POS) as input words and treating large numbers of training documents. This was caused by the overfitting problem that SVM sometimes selected unsuitable support vectors for each category in the training set. To avoid the overfitting problem, we propose a two step text categorization method with a variable cascaded feature selection (VCFS) using SVM. VCFS method selects a pair of the best number of words and the best POS combination for each category at each step of the cascade. We made use of the difference of words with the highest mutual information for each category on each POS combination. Through the experiments, we confirmed the validation of VCFS method compared with other SVM based text categorization methods, since our results showed that the macro-averaged F1 measure (64.8%) of VCFS method was significantly better than any reported F1 measures, though the micro-averaged F1 measure (85.4%) of VCFS method was similar to them.

Since a logic circuit often has too many paths to test delay of all paths, it is necessary for path delay testing to limit the number of paths to be tested. The paths to be tested should have large delay because such paths more likely cause a fault. Additionally, a test set for the paths are required to detect other models of faults as many as possible. In this paper, we investigate two typical criteria of path selection for path delay testing. From our experiments, we observe that test patterns for the longest paths cannot cover many local delay defects such as transition faults.

Space-Time Turbo code is an effective method for the enhancement of link capacity and maximizing the link-budget by balancing the coding gain obtained via Turbo codes and the diversity gain obtained through multiple antenna transmission. A study on an antenna selection scheme for Space-Time Turbo code for OFDM systems is presented in this paper. In the proposed method, the systematic bits and the punctured parity bits are sent from the selected antenna for each sub-carrier, while data transmission is suspended from the antenna experiencing poor channel conditions at the receiver. Simulation results show that the proposed method yields a 2.2 dB gain in the required TxEb/N0 relative to the conventional method, and makes the channel estimation accuracy more robust. Moreover, the proposed method reduces transmission power by about 4 dB compared to the conventional method.

In supervised learning, the selection of sample points and models is crucial for acquiring a higher level of the generalization capability. So far, the problems of active learning and model selection have been independently studied. If sample points and models are simultaneously optimized, then a higher level of the generalization capability is expected. We call this problem active learning with model selection. However, active learning with model selection can not be generally solved by simply combining existing active learning and model selection techniques because of the active learning/model selection dilemma: the model should be fixed for selecting sample points and conversely the sample points should be fixed for selecting models. In this paper, we show that the dilemma can be dissolved if there is a set of sample points that is optimal for all models in consideration. Based on this idea, we give a practical procedure for active learning with model selection in trigonometric polynomial models. The effectiveness of the proposed procedure is demonstrated through computer simulations.

In this paper, a non-adaptive optimal transform (NAOT) coding system is proposed. Note that the energy-invariant property in an orthogonal transformation and that the mean squared error (MSE) of a reconstructed image is proportional to the total energy of transform coefficients discarded in the coding process. The NAOT coding system is developed and proved optimal in the sense of minimum average energy loss. Basically, the proposed coding system consists of the following steps. First, obtain the average energy image block from transform image blocks. Second, sort the average energy image block in the descending order by energy where the sorted indices are recorded. Third, specify the number of coefficients, M, to be retained in the coding process. Fourth, the first M sorted indices form a set denoted as SM through which the problem of optimal feature selection in transform coding is solved. Fifth, find a fixed mask AM from set SM which is then used to select M significant transform coefficients in image blocks. Finally, the M selected coefficients are quantized and coded by the order as in SM. To verify the NAOT coding system, simulations are performed on several examples. In the simulation, the optimality and the optimal feature selection in the NAOT coding system are justified. Also, the effectiveness of the proposed SM-based selection approach is compared with the zigzag scan used in the JPEG. For fair comparison, the JPEG is modified to code only M transform coefficients. Simulation results indicate that the performance of SM-based selection approach is superior or identical to the zigzag scan in terms of PSNR. Finally, the performance comparison between the NAOT coding system and the JPEG is made. It suggests that the proposed NAOT coding system is able to trade very little PSNR for significant bit rate reduction when compared with the JPEG. Or it can be said that the JPEG wastes much bit rate to improve very little PSNR on the reconstructed image, when compared with the NAOT coding system.

This paper proposes a framework for building a reusable mobile agent from two kinds of components: an itinerary component and an application-specific logic component. Both components are implemented as mobile agents. The former is a carrier of the latter over particular networks and the latter defines management tasks performed at each host independently of the network. This framework also provides a mechanism for matchmaking the two mobile agent-based components. Since the mechanism is formulated based on a process algebra approach, it can strictly select a suitable itinerary component to perform management tasks at the hosts that the tasks want to visit over the networks. A prototype implementation of this framework and its application were built on a Java-based mobile agent system. We summarize the lessons learned while formalizing and implementing the approach and we compare it to related work.

As so many software titles are now being distributed via the Internet, the number of accesses to file servers, such as FTP servers, is rapidly increasing. To prevent the concentration of accesses to the original file server, mirror servers are being introduced that contain the same directories and files as held by the original server. However, inconsistency among the mirror servers and the original server is often observed because of delivery latency, traffic congestion on the network, and management policies of the mirror servers. This inconsistency degrades the value of the mirror servers. Accordingly, we have developed an intermediate FTP proxy server system that guarantees the freshness of the files as well as preventing access concentration on the original FTP server. The system adopts per-file selection of the replicated files; most existing methods are based on per-host or per-directory selection. Therefore it can assure users of a quick, stable, and up-to-date FTP mirroring service even in the face of frequent content updates, which tend to degrade the homogeneity of services. Moreover, it can forward the retrieved files with little overhead. Tests confirmed that our system is comparable to existing systems from the viewpoint of actual retrieval time, required traffic, and load endurance. This technology can assure clients that they will receive the latest version of the file(s) desired. It well supports heterogeneous network environments such as the Internet.

NK-Landscapes are stochastically generated fitness functions on bit strings, parameterized with N bits and K epistatic interactions between bits. The term epistasis describes nonlinearities in fitness functions due to changes in the values of interacting bits. Empirical studies have shown that the overall performance of random bit climbers on NK-Landscapes is superior to the performance of some simple and enhanced genetic algorithms (GAs). Analytical studies have also lead to suggest that NK-Landscapes may not be appropriate for testing the performance of GAs. In this work we study the effect of selection, drift, mutation, and recombination on NK-Landscapes for N = 96. We take a model of generational parallel varying mutation GA (GA-SRM) and switch on and off its major components to emphasize each of the four processes mentioned above. We observe that using an appropriate selection pressure and postponing drift make GAs quite robust on NK-Landscapes; different to previous studies, even simple GAs with these two features perform better than a random bit climber (RBC+) for a broad range of classes of problems (K 4). We also observe that the interaction of parallel varying mutation with crossover improves further the reliability of the GA, especially for 12 < K < 32. Contrary to intuition, we find that for small K a mutation only evolutionary algorithm (EA) is very effective and crossover may be omitted; but the relative importance of crossover interacting with varying mutation increases with K performing better than mutation alone (K > 12). This work indicates that NK-Landscapes are useful for testing each one of the major processes involved in a GA and for assessing the overall behavior of a GA on complex non-linear problems. This study also gives valuable guidance to practitioners applying GAs to real world problems of how to configure the GA to achieve better results as the non-linearity and complexity of the problem increases.

This paper describes a practical Japanese natural language Question Answering system adopting effective selection of dynamic passages, Lexico-Semantic Patterns (LSP), and Predictive Answer Indexing. By analyzing the previous TREC QA data, we defined a dynamic passage unit and developed a passage selection method suitable for Question Answering. Using LSP, we identify the answer type of a question and detect answer candidates without any deep linguistic analyses of the texts. To guarantee a short response time, Predictive Answer Indexing is combined into our overall system architecture. As a result of the three engineering techniques, our system showed excellent performance when evaluated by mean reciprocal rank (MRR) in NTCIR-3 QAC-1.

MC-CDMA has attracted significant attention as a downlink communication method for fourth generation mobile communication systems. However, MC-CDMA has a peak power problem, similarly to other nonspread multicarrier systems. In this paper, we propose a novel peak power reduction technique for MC-CDMA using code selection at the transmitter. In the proposed system, the transmitter selects the code shift pattern from plural pre-assigned code shift patterns in each cluster to minimize the output peak power of downlink signals. The proposed technique can achieve the blind code detection of transmitting codes at the receiver. However, the detection performance degrades if the number of users is extremely large or extremely small. Therefore, in this paper, a code detection error recovery technique is also considered.

This paper proposes a disjoint path selection scheme for Generalized Multi-Protocol Label Switching (GMPLS) networks with Shared Risk Link Group (SRLG) constraints. It is called the weighted-SRLG (WSRLG) scheme. It treats the total number of SRLG members related to a link as part of the link cost when the k-shortest path algorithm is executed. In WSRLG, a link that has many SRLG members is rarely selected as the shortest path. Simulation results show that WSRLG finds more disjoint paths than the conventional k-shortest path algorithm. In addition, since WSRLG searches for the weight of the SRLG factor by using a modified binary search algorithm while satisfying the required number of disjoint paths between source and destination nodes, it can find cost-effective disjoint paths.

In this paper, a simple subcarrier selection combining technique is proposed for orthogonal frequency-division multiplexing (OFDM) systems with multiple receive antennas. The subcarrier-based selection algorithm is developed in the frequency domain to achieve an optimal selection combining gain for OFDM systems, instead of the antenna-based selection algorithms in the time domain or frequency domain. The proposed technique selects an optimal subcarrier with a maximum channel gain among all the receive antennas with the same subcarrier position, based on the estimated channel frequency response during the training period. Hardware complexity for the proposed technique is minimal since it requires single front-end with multiple receive antennas and single baseband demodulator. It is shown by computer simulation that a significant gain can be achieved by the proposed technique over the conventional selection combining technique for OFDM systems in practical situations.

In the problem of determining the major frequency components of a signal disturbed by noise, a model selection criterion has been proposed. In this paper, the criterion has been extended to cover a penalized cost function that yields a componentwise shrinkage estimator, and it exhibited a consistent model selection when the proposed criterion was used. Then, a simple numerical simulation was conducted, and it was found that the proposed criterion with an empirically estimated componentwise shrinkage estimator outperforms the original criterion.

In content distribution networks (CDNs), the content routing which directs user requests to an adequate server from the viewpoint of improvement of latency for obtaining contents is one of the most important technical issues. Several information, e.g. server load or network delay, can be used for content routing. Network support, e.g. active network, enables a router to select an adequate server by using these information. In the paper, we investigate a server selection policy of a network support approach from the viewpoint of which information to be used for effective server selection. We propose a server selection policy using RTT information measured at a router. Simulation results show that our proposed server selection policy in content routing selects a good server under both conditions where server latency and network delay is a dominant element of user response time. Furthermore, we also investigate about location of routers with network support bringing good performance for our proposed scheme.

Load balancing among multiple mirror servers located at distributed positions in the network is a key technique for content delivery services. For bandwidth allocated services, we consider how to select a suitable server from several candidates containing the same content at the time of a request. We propose limiting the candidates in advance and selecting a server from the limited set of servers in a round-robin fashion. The server sets that minimize the variance of the link load are derived using a greedy method for a given network topology and service demand. Through numerical evaluation, we demonstrate that the proposed method is superior to previous methods.

Transmit diversity schemes are an effective capacity improvement method for down link of wideband code division multiple access (W-CDMA) systems. In this paper, we propose to use transmit antenna subset selection scheme in conjunction with closed loop transmit adaptive array (TxAA). The proposed scheme selects NS optimum antennas among NT (>NS) transmit antennas in order to maximize diversity gain from the selected antennas, and also reduces the cost of RF chains by employing two different types of RF modules for the selected and the unselected antenna group, respectively. Computer simulation results show performance improvement by the proposed scheme over the conventional TxAA when considering up link control information feedback.

In this paper, we propose a new transmitter diversity. We propose a combined system with path diversity gain of the distributed antennas and frequency diversity gain of the multi-carrier. The proposed system transmits different data using several sub-carriers which are correlated, while, transmitting the same data using several sub-carriers which are decorrelated. It can achieve combined path and frequency diversity in a variable frequency selective fading channel. It provides high data rate services by transmitting the different data using each correlated carrier, and supports good quality by transmitting the same data on decorrelated carriers using multiple antennas. The proposed system is applicable to multimedia service and can achieve high quality according to channel condition. Thus, the proposed system is sufficiently flexible enough to very support a variety of video, image, voice and data services at a high level of quality.

Transmit adaptive array requires the forward link channel state for evaluating the optimum transmit weight in which a feedback channel transports the forward link channel state to the base station. Since the feedback information limits the transmission rate of the reverse link traffic, it is necessary to keep the number of feedback bits to a minimum. This paper presents a system in which the N transmit antennas are extended to the 2N transmit antennas while the feedback channel is limited as that of N-transmit antenna system. The increased antennas can give additional diversity gain but requires higher rate of feedback bits. The limited feedback channel increases the quantization error of feedback information since the number of feedback bits assigned to each antenna is reduced. In order to overcome the limited rate of feedback channel problem, this paper proposes the transmit antenna selection schemes which can effectively use the limited feedback bits, reduce the computational complexity at the mobile station, and eventually achieve diversity gain. System performances are investigated for the case of N=4 for the various antenna selection schemes on both flat fading and multi-path fading channels.