Compression-based Dissimilarity Measure (CDM) is reported to work well in classifying strings without clues. However, CDM depends on the compression program, and its theoretical background is unclear. In this paper, we propose to replace CDM with the computation of information quantity. Since CDM only uses compressed size, our approach uses the value of information quantity of maximum probability partitioning of string instead of file size. We find this approach is more effective. Then, CDM and the proposed method were applied to publicly available time series data. In addition to the careful implementation of computation using suffix arrays, we also find this approach more efficient.

We present Pic2PLex, a new test for assessing free productive vocabulary, the lexicon people use for writing and speaking. Unlike the existing productive vocabulary tests, Pic2PLex uses images as stimuli to let test-takers say something. A test item comprises a set of six different images with a common theme and two answer sections: a tenword section and a description section. Given the image set, test-takers are instructed to write ten words that come to their minds and a brief description of the images in the corresponding sections. We propose an algorithm to generate image sets for the test items from a large image dataset. The algorithm was designed to simultaneously achieve two conflicting goals: making images of a test item share the same theme and making themes among image sets across test items diverse as much as possible. We conducted experiments with Japanese language learners and native Japanese speakers to collect responses using Pic2PLex and existing productive vocabulary tests for comparison. Quantitative and qualitative investigation revealed that (i) Pic2PLex elicits words based on productive lexical knowledge that the test-takers would have acquired, and (ii) the elicited responses can be used in assessments that measure how much productive vocabulary the test-taker has.

The recent development of mobile applications offering ride-hailing services has made large-scale taxi usage data available, and dispatching and pricing technologies based on such data have advanced greatly. While existing methods have achieved some success, they ignore an important perspective: individual priorities in terms of time and money savings. For example, existing methods may dispatch a distant taxi to a customer who prioritizes promptness over cost, and a high-priced taxi to a customer who wants to save money more than time. These mismatches degrade the requester’s utility and the service provider’s profit. In this paper, we propose a new framework for determining price and time proposals based on the individual’s preferences in ride-hailing services. First, we formulate a new optimization problem to yield better price and time proposals for requesters based on their priorities. We model the requester’s taxi acceptance probabilities by the generalized cost model and the discrete choice model, both of which are widely used in transportation economics. The price and time proposals yielded by solving our problem achieve high requester satisfaction because price and time proposals are created to suit for each requester. They raise service provider profits because the proposals are less likely to be rejected. Although the discontinuity of the objective function and the difficulty of its evaluation make our problem difficult to solve, we propose a fast approximation algorithm. The proposed algorithm outputs an L-approximate solution in O(n・(n3 +m3)) time, where L is a hyperparameter controlling the relative weighting of requester satisfaction and service provider profit, n is the number of requesters, and m is the number of taxis. Simulations on synthetic and real datasets show that our method increases the requester’s satisfaction and the service provider’s profit simultaneously with practical computation times.