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A web-based Java programming learning assistant system (JPLAS) has been developed for novice students to study Java programming by themselves while enhancing code reading and code writing skills. One type of the implemented exercise problem is code writing problem (CWP), which asks students to create a source code that can pass the given test code. The correctness of this answer code is validated by running them on JUnit. In previous works, a Python-based answer code validation program was implemented to assist teachers. It automatically verifies the source codes from all the students for one test code, and reports the number of passed test cases by each code in the CSV file. While this program plays a crucial role in checking the correctness of code behaviors, it cannot detect code plagiarism that can often happen in programming courses. In this paper, we implement a code plagiarism checking function in the answer code validation program, and present its application results to a Java programming course at Okayama University, Japan. This function first removes the whitespace characters and the comments using the regular expressions. Next, it calculates the Levenshtein distance and similarity score for each pair of source codes from different students in the class. If the score is larger than a given threshold, they are regarded as plagiarism. Finally, it outputs the scores as a CSV file with the student IDs. For evaluations, we applied the proposed function to a total of 877 source codes for 45 CWP assignments submitted from 9 to 39 students and analyzed the results. It was found that (1) CWP assignments asking for shorter source codes generate higher scores than those for longer codes due to the use of test codes, (2) proper thresholds are different by assignments, and (3) some students often copied source codes from certain students. [ABSTRACT FROM AUTHOR]

• Template-matching and optical character recognition technics were adopted. • The number of template images to identify all the manufacturers was about 50%. • The rate of model-name identification attained 92% (for static images). • The rate of model-name identification was 81% (for moving images). In an attempt to select an efficient recycling process for waste electrical and electronic equipment based on the value of individual products, we are engaged in the development of an automatic object-recognition system for discarded equipment. As part of this initiative, we developed a new object-recognition algorithm that uses the information from the labels on the bottoms of digital cameras discarded in Japan, which have a relatively high value. In addition, we created a program that can continuously process multiple two-dimensional digital images of the bottoms of the discarded cameras. The algorithm developed consists of the following: 1. Identifying the manufacturer using template matching with the manufacturer's logo on the label as a template image; 2. reading the model name located close to the logo using optical character recognition (OCR) processing; and 3. extracting the model-name candidates via a similarity calculation between the result of the OCR and the model-name list. After analyzing the information on the label of the discarded cameras, we carried out an object-recognition test using the images captured inside a photography box. The results demonstrated that on average, 48% of the total number of template images was necessary to identify all the manufacturers. This value varies from manufacturer to manufacturer; however, the template image with the "highest versatility" correctly matched 42% of the models of a certain manufacturer. The model-name identification for each manufacturer was successful 92% of the time on average, which indicated the effectiveness of this algorithm and emphasized the necessity of extracting the model-name candidates from the OCR result. Finally, assuming that a continuous process will be feasible in the future, a test was carried out using the photographed images of the discarded cameras moving on a conveyor belt at a speed of 0.5 m/s. The results demonstrated that the percentage of the number of template images required to identify the manufacturer was almost identical to that for static images. Notwithstanding the limitations of the image resolution (58% lower than that of the still images), the model-name identification rate was 81%. [ABSTRACT FROM AUTHOR]