Bio: Takaaki Goto is an academic researcher from Ryutsu Keizai University. The author has contributed to research in topics: Software development & Software construction. The author has an hindex of 7, co-authored 46 publications receiving 142 citations. Previous affiliations of Takaaki Goto include Toyo University & Winona State University.
24 Jul 2017
TL;DR: The proposed model gradually minimizes two different objective functions; namely the root mean square error (RMSE) and Maximum Error in order to find the optimal weight vector for the artificial neural network (ANN) to improve its performance over its traditional counterparts.
Abstract: Domestic and industrial pollutions affected the water quality to a greater extent. Polluted water became a major reason behind several community diseases, mainly in undeveloped and developing countries. The public health condition is deteriorating and putting an extra burden of countermeasures to prevent such water borne diseases from spreading. Detecting the drinking water quality can prevent such scenarios prior to the critical stage. Recent research works have achieved reasonable success in predicting the water quality. However, the accuracy levels of already proposed models are to be improved, keeping in mind the sensitivity of the problem domain. In the current work, multi-objective genetic algorithm was employed to train the artificial neural network (NN-MOGA) to improve its performance over its traditional counterparts. The proposed model gradually minimizes two different objective functions; namely the root mean square error (RMSE) and Maximum Error in order to find the optimal weight vector for the artificial neural network (ANN). The proposed model was compared with three other, well established models namely NN-GA (ANN trained with Genetic Algorithm), NN-PSO (ANN trained with Particle Swarm Optimization) and SVM in terms of accuracy, precision, recall, F-Measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows index (FM index). The simulation results established superior accuracy of NN-MOGA over the other models.
••01 Sep 2014
TL;DR: This paper proposes a plagiarism detection method which is not influenced by changing the identifier or program statement order, and explains its capabilities by comparing it to the sim plagiarism detector.
Abstract: Learning to program is an important subject in computer science courses. During programming exercises, plagiarism by copying and pasting can lead to problems for fair evaluation. Some methods of plagiarism detection are currently available, such as sim. However, because sim is easily influenced by changing the identifier or program statement order, it fails to do enough to support plagiarism detection. In this paper, we propose a plagiarism detection method which is not influenced by changing the identifier or program statement order. We also explain our method's capabilities by comparing it to the sim plagiarism detector. Furthermore, we reveal how our method successfully detects the presence of plagiarism.
••13 Jun 2018
TL;DR: A generic framework is proposed in this paper so that different NoSQL databases could be converted to RDBMS, and a case study on MongoDB and Neo4j proves robustness of the proposed mechanism.
Abstract: Due to the exponential growth of NoSQL databases and in addition the circumstance of perusing humongous volumes of information, maximum applications switch RDBMS to NoSQL and pick it as information stockpiling framework. But we all know that RDBMS have several advantages which make it a popular platform across several applications over the decades. Therefore we view the standard problem of converting the RDBMS to NoSQL in reverse approach and we conceptualize a problem where NoSQL is converted back to a RDBMS based system. A generic framework is proposed in this paper so that different NoSQL databases could be converted to RDBMS. This approach is illustrated here using a case study on MongoDB and Neo4j. MongoDB is a document oriented database, fully unstructured and schemaless whereas Neo4j is a graph oriented database, fully unstructured and schemaless. This proves robustness of our proposed mechanism.
TL;DR: A construction site work management system that reduces the burden of input from mobile phones in cooperation with construction companies from the viewpoint of user-centered design and two support tools are devised.
Abstract: In construction work, information systems that use mobile communications are required in order to elimi- nate the troublesome task of writing and retyping data and to acquire realtime data. The information must be shared effectively. Therefore, we developed a construction site work management system that reduces the burden of input from mobile phones in cooperation with construction companies from the viewpoint of user-centered design. The system's main features are easy data input from mobile phones and functions for authentication, data input, data retrieval, data update/deletion, and graphic representation of work progress reports. Input data is stored in a database over the Internet, enabling the shared data to be used to carry out work smoothly. At an early stage of development after we demonstrated an early version of the system, we circulated a questionnaire to construction companies so that we could incorporate their opinions. Responses from construction workers showed that easy data input is important. Therefore, we devised three methods for reducing the burden of input from mobile phones and two support tools. Furthermore, we evaluated an encoding method and a method using position information.
TL;DR: In this paper, a precedence graph grammar for Hichart is presented, which can parse program diagrams in linear time, and a parsing method and processing system incorporating the HichART graphical editor is described.
Abstract: In software design and development, program diagrams are often used for good visualization. Many kinds of program diagrams have been proposed and used. To process such diagrams automatically and efficiently, the program diagram structure needs to be formalized. We aim to construct a diagram processing system with an efficient parser for our program diagram Hichart. In this paper, we give a precedence graph grammar for Hichart that can parse in linear time. We also describe a parsing method and processing system incorporating the Hichart graphical editor that is based on the precedence graph grammar.
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
01 Jan 2002
17 Mar 2011
TL;DR: Reading user centered system design is a good habit; you can develop this habit to be such interesting way to be one of guidance of your life.
Abstract: Will reading habit influence your life? Many say yes. Reading user centered system design is a good habit; you can develop this habit to be such interesting way. Yeah, reading habit will not only make you have any favourite activity. It will be one of guidance of your life. When reading has become a habit, you will not make it as disturbing activities or as boring activity. You can gain many benefits and importances of reading.
TL;DR: Software watermarking as discussed by the authors protects software through embedding some secret information into software as an identifier of the ownership of copyright for this software, which is a type of digital objects.
Abstract: In the Internet age, software is one of the core components for the operation of network and it penetrates almost all aspects of industry, commerce, and daily life. Since digital documents and objects can be duplicated and distributed easily and economically cheaply and software is also a type of digital objects, software security and piracy becomes a more and more important issue. In order to prevent software from piracy and unauthorized modification, various techniques have been developed. Among them is software watermarking which protects software through embedding some secret information into software as an identifier of the ownership of copyright for this software. This paper gives a brief overview of software watermarking. It describes the taxonomy, attack models, and algorithms of software watermarking.