Debugging erroneous programs requires a great deal of human effort. To reduce human effort, automating debugging processes has been actively studied so far. One of such automation is automated program repair techniques for syntactic errors in programs. Researchers intend to support novice programmers such as students with these techniques because fixing syntactic errors is a difficult task for novice programmers. However, there exist few datasets that consist of programs written by novice programmers in universities and can be used to evaluate these techniques. Also, it is difficult to prepare such datasets from scratch. Indeed, prior studies usually utilized the Indian Institute of Technology Kanpur (IITK) dataset only. This limitation restricts the findings and implications in prior studies as a case study in the university. In this study, we intend to clarify which findings and implications in prior studies remain the same and which ones change in another university by a case study. We prepare three datasets that consist of over 21k programs collected from an introductory programming course in different divisions at our university. We compare the state-of-the-art automated program repair techniques, DeepFix, RLAssist, and Dr Repair, in these datasets. We found that (1) the best technique remains the same in all the datasets, (2) these techniques fix 8.3 % to 54.5 % syntactic errors in our datasets, which are 6.7 % to 32.4 % lower than those in the IITK dataset, (3) the error types that are fixed by them change in different datasets. Hence, the main finding in the IITK dataset remains the same; however, each technique fixes different errors.

Summary: We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences. Availability and implementation: This feature is available in MAFFT versions 7.355 or later at https://mafft.cbrc.jp/alignment/software/mpi.html. Supplementary information: Supplementary data are available at Bioinformatics online.

In this paper, we propose a method of obtaining the sense of touch by using the EEG. Multimodal device based on human sensory systems have obtained a lot of attention in the fields of human interface. Among the human sensory systems, especially, the sense of touch is important form of social interaction and it can have powerful emotional consequences. Therefore, it is important to improve tactile sensation device technique and evaluate the device objectively. By analyzing the EEG, we attempt to make the evaluation method clear how users feel when he/she touches objects. We measure the EEG while subjects touch objects by using portable electroencephalograph. The proposed method consists of feature extraction and EEG pattern classification by using fast fourier transform(FFT) and artificial neural network(ANN). Moreover, we apply particle swarm optimization(PSO) for deciding the weight and bias of the ANN fastly.