Software development companies must consider employees' job satisfaction and turnover intentions. To explain the related factors, this study focused on future perspective index (FPI). FPI was assumed to relate positively to satisfaction and negatively to turnover. In the analysis, we compared the FPI with existing factors that are considered to be related to job satisfaction. We discovered that the FPI was promising for enhancing explanatory power, particularly when analyzing satisfaction.

In this paper, the differences in navigation information design, which is important for kiosk-type pedestrian navigation systems, were experimentally examined depending on presence or absence of carriable navigation information in order to acquire the knowledge to contribute design guidelines of kiosk-type pedestrian navigation systems. In particular, we used route complexity information calculated using a regression equation that contained multiple factors. In the absence of carriable navigation information, both the destination arrival rate and route deviation rate improved. Easy routes were designed as M (17 to 39 characters in Japanese), while complicated routes were denoted as L (40 or more characters in Japanese). On the contrary, in the presence of carriable navigation information, the user's memory load was found to be reduced by carrying the same navigation information as kiosk-type terminals. Thus, the reconsideration of kiosk-type pedestrian navigation systems design, e.g., the means of presenting navigation information, is required. For example, if the system attaches importance to a high destination arrival rate, L_Carrying without regard to route complexity is better. If the system attaching importance to the low route deviation rate, M_Carrying in the case of easy routes and L_Carrying in the case of complicated routes have been better. Consequently, this paper presents the differences in the designs of pedestrian navigation systems depending on whether carriable navigation information is absent or present.

Overlay routing is an application-level routing mechanism on overlay networks. Previous researches have revealed that the overlay routing can improve user-perceived performance. However, it may also generate traffic unintended by ISPs, incurring additional monetary cost. In addition, since ISPs and end users have their own objectives respectively regarding traffic routing, overlay routing must be operated considering both standpoints. In the present paper, we propose a method to reduce inter-ISP transit costs caused by overlay routing from the both standpoints of ISPs and end users. To determine the relationships among ASes, which are required for ISP cost-aware routing, we construct a method to estimate a transit cost of overlay-routed paths from end-to-end network performance values. Utilizing the metric, we propose a novel method that controls overlay routing from the both standpoints of ISPs and end users. Through extensive evaluations using measurement results from the actual network environments, we confirm that the advantage of the proposed method whereby we can reduce the transit cost in the overlay routing and can control the overlay routing according to the objectives of both ISPs and end users.

In this paper we show that the performance workload of button-input interfaces do not monotonically increase with the number of buttons, but there is an optimal number of buttons in the sense that the performance workload is minimized. As the number of buttons increases, it becomes more difficult to search for the target button, and, as such, the user's cognitive workload is increased. As the number of buttons decreases, the user's cognitive workload decreases but his operational workload increases, i.e., the amount of operations becomes larger because one button has to be used for plural functions. The optimal number of buttons emerges by combining the cognitive and operational workloads. The experiments used to measure performance were such that we were able to describe a multiple regression equation using two observable variables related to the cognitive and operational workloads. As a result, our equation explained the data well and the optimal number of buttons was found to be about 8, similar to the number adopted by commercial cell phone manufacturers. It was clarified that an interface with a number of buttons close to the number of letters in the alphabet was not necessarily easy to use.

Stochastic approaches for effective power distribution network optimization are proposed. Considering node voltages obtained using dynamic voltage drop analysis as sample variables, multi-variate regression is conducted to optimize clock timing metrics, such as clock skew or jitter. Aggregate correlation coefficient (ACC) which quantifies connectivity between different chip regions is defined in order to find a possible insufficiency in wire connections of a power distribution network. Based on the ACC, we also propose a procedure using linear regression to find the most effective region for improving clock timing metrics. By using the proposed procedure, effective fixing point were obtained two orders faster than by using brute force circuit simulation.

There is still much problem in sizing software despite the existence of well-known software sizing methods such as Function Point method. Many developers still continue to use ad-hoc methods or so called "expert" approaches. This is mainly due to the fact that the existing methods require much information that is difficult to identify or estimate in the early stage of a software project. The accuracy of ad-hoc and "expert" methods also has much problem. The entity-relationship (ER) model is widely used in conceptual modeling (requirements analysis) for data-intensive systems. The characteristic of a data-intensive system, and therefore the source code of its software, is actually well characterized by the ER diagram that models its data. This paper proposes a method for building software size model from extended ER diagram through the use of regression models. We have collected some real data from the industry to do a preliminary validation of the proposed method. The result of the validation is very encouraging.

This paper describes a new multi-channel method of noisy speech recognition, which estimates the log spectrum of speech at a close-talking microphone based on the multiple regression of the log spectra (MRLS) of noisy signals captured by distributed microphones. The advantages of the proposed method are as follows: 1) The method does not require a sensitive geometric layout, calibration of the sensors nor additional pre-processing for tracking the speech source; 2) System works in very small computation amounts; and 3) Regression weights can be statistically optimized over the given training data. Once the optimal regression weights are obtained by regression learning, they can be utilized to generate the estimated log spectrum in the recognition phase, where the speech of close-talking is no longer required. The performance of the proposed method is illustrated by speech recognition of real in-car dialogue data. In comparison to the nearest distant microphone and multi-microphone adaptive beamformer, the proposed approach obtains relative word error rate (WER) reductions of 9.8% and 3.6%, respectively.

This paper describes techniques of scoring prosodic proficiency of English sentences spoken by Japanese. The multiple regression model predicts the prosodic proficiency using new prosodic measures based on the characteristics of Japanese novice learners of English. Prosodic measures are calculated by comparing prosodic parameters, such as F0, power and duration, of learner's and native speaker's speech. The new measures include the approximation error of the fitting line and the comparison result of prosodic parameters for a limited segment of the word boundary rather than the whole utterance. This paper reveals that the introduction of the new measures improved the correlation by 0.1 between the teachers' and automatic scores.

A new method is proposed for realizing a flexible change detection which is free from the limitation that multitemporal images must have the same spectral bands whose center wavelength and bandwidth are identical. As spaceborne multispectral scanners are continuously improved for performance and new scanners do not necessarily have the same spaectral bands for observation, this limitation is a serious obstacle for detecting long term temporal change. The proposed method removes this limitation by using an image normalization technique based on multiple regression analysis. The method is successfully applied to actual remotely sensed multitemporal images.

In case of object recognition using 3-D configuration data, the scale and poses of the object are important factors. If they are not known, we can not compare the object with the models in the database. Hence we propose a strategy for object recognition independently of its scale and poses, which is based on Hopfield neural network. And we also propose a strategy for estimation of the camera motion to reconstruct 3-D configuration of the object. In this strategy, the camera motion is estimated only with the sequential images taken by a moving camera. Consequently, the 3-D configuration of the object is reconstructed only with the sequential images. And we adopt the multiple regression analysis for estimation of the camera motion parameters so as to reduce the errors of them.