Since software quality model is defined, many quality assurance teams use the model of analysis of software products for quality control. In case of DevOps, a pipeline is built in the CI/CD process, testing work is placed in the pipeline, and automation of testing work is promoted for efficient product development, but most of DevOps projects have used classic quality metrics, as like bug ratio, test coverage rate so on. By categorizing the quality ensured by CI/CD pipeline testing by quality characteristics, the progress of the quality required for the released system can be visualized and controlled by quality model. In addition, by defining software quality goals with quality characteristics before the project starts, it is possible to use quality characteristics as KPIs. Using quality characteristics as KPIs needs to manage the results of tests in each pipeline and compare with results with past builds. This paper explains how to visualize the quality ensured by the CI/CD process, which is indispensable for DevOps, as well as the advantages of using quality characteristics as KPIs, and proposes method of achievement for rapid and high-quality products development.

As described herein, we propose a method to make more accurate predictions based on COVID-19 positive case data from Tokyo, which are provided as open data. Our proposed method uses prediction results of related variables to infer an objective function. Prediction of the number of infected people in Tokyo based on this method yielded better correlation between the predicted results and the actual number of COVID-19 positive cases than prediction of the number of infected people. Results also showed better correlation between prediction results and the actual number of COVID-19 positive cases than prediction based on the number of infected cases alone, indicating that our prediction method provides higher accuracy.

The spacecraft is a mass of sensors, and there are a vast number of operational commands. In order to apply anomaly detection to multiple spacecraft across the missions, an event detection method is required using the only telemetry that does not use prior design information. This report has verified a simple method to detect the enhanced area in the telemetry using only command log and telemetry. It can be a powerful technique for testing that the command was executed correctly without design information.

Over-tourism has become an important difficulty in Japan because the number of visiting international tourists has increased in recent years. This intensive tourism leads to sightseeing problems because opportunities to inform tourists about culture and rules in tourist areas are few. Some system is needed to convey correct cultural aspects of tourist areas. This paper proposes a system to present a user with useful information such as area- specific culture from photographs taken with a convolutional neural network (CNN). Tourists can gain information by associating the contents with the real world by browsing useful information while viewing photographs. After we constructed the prototype system to present 30 types of useful information in English, we evaluated our system quantitatively. We also administered a questionnaire survey for Japanese and foreign residents. The results demonstrate that our system is effective to facilitate foreign tourists' understanding Japanese culture and norms.

One difficulty of car-sharing services is trouble among users related to damage of the automobile. With the rapid growth of car-sharing services, demand exists for a new approach for automatic detection of the occurrence of damage during crewless operation. In this study, we propose a method for automatic detection of abnormal vibration that damages an automobile that occurs driving and stopping. After using multiple piezoelectric sensors installed in the automobile to acquire vibration, we applied supervised learning to extract features from vibrations and to detect abnormal vibrations. The proposed method achieved recall of 0.96 for stopped automobiles and recall of 0.82 for driving automobiles.

Analytic methodology as to generation of integrated hypotheses is necessary for applications involving different sources of social big data. In this paper, first, we introduce an abstract data model for integrating data management and data mining by using mathematical concepts of families, collections of sets to facilitate reproducibility and accountability required for social big data applications. Next, we describe generalized difference methods as a methodology for generating integrated hypotheses. Finally, we validate our proposal by applying them to three use cases involving data in concept and real spaces by using our data model as their description guided by generalized difference methods.

Owing to the increasing popularity of mobile devices embedded with a Global Positioning System (GPS) sensor, large amounts of user-generated content containing spatial information have been uploaded to social media websites, such as Flickr or Twitter. That content posted from tourist spots can be used to search for and recommend other tourism spots and routes. Recent research papers in the field of Natural Language Processing (NLP) have proposed learning a distributed representation of words using embedding algorithms. In this paper, we use a Skip-gram model to analyze user movements obtained from social media websites. We propose a new Skip-gramgemodel to learn movements between a pair of locations quantified by the latitude and the longitude. The embedding vectors by our model represents user movements between the locations. We successfully demonstrated that the embedded vectors generated with our proposed method can extract detour spots for tourism spots and routes.

Attempts to improve database performance have led to the use of high-speed and large nonvolatile memory as a durable database medium instead of existing storage devices. For such database systems, the cost of memory access instead of I/O processing decreases, and the CPU cost increases relative to the most suitable join method selected for a database query to minimize query execution time. Moreover, for reducing database administration overhead, the cost model is also required to be capable of application to different generation CPUs through minor modification. In this paper, the measurement-based cost calculation method (MBCC) for solving these requirements is described. This cost calculation formula using MBCC is based on the behavior of the instruction issuing part in the CPU instruction pipeline and the tendency between the statistical information measured by the CPU performance monitor The cost calculation formulas are formed into parts for each element separated into elements repeatedly appearing in the access path of the join, and the cost is calculated combining the parts for an arbitrary number of join tables. In addition, MBCC enables the join cost calculation formula to support a CPU with architecture from a different generation without the need to re-measure the statistical information of the CPU. The evaluation of the accuracy of MBCC cost calculations revealed that the difference between the predicted cross point and the measured cross point was reduced by 74% to 95% compared with the difference between the cross point obtained by the conventional method and the measured cross point, and the updated cost evaluation formulas estimated the cost of joining different generation-based CPUs accurately in 66% of the test cases. In conclusion, the database system using the proposed cost calculation method can select the best join method and can be applied with CPUs from different generations.

Recently there is strong demand for analytic methodology as to generation of integrated hypotheses for applications involving different sources of social big data. In this paper, first, we introduce an abstract data model for integrating data management and data mining by using mathematical concepts of families, collections of sets to facilitate reproducibility and accountability required for social big data applications. Next, we propose generalized difference methods as a methodology for integrated analysis based on different sources of data. Finally, we validate our proposal by applying it to three use cases by using our data model as their description.

We show the dynamics of scale-free nature of human daily operations on a personal computer i.e. keystrokes and mouse clicks or wheels. A time-interval t between successive operations obeys a power-law type probability distribution p(t)oct-α in a range from 100 to 104 seconds. The scaling exponent α is empirically shown to vary depending on variation of daily worklad n: the number of daily operations, in the form of α ∝ log n. With the aid of a modified fractal renewal process, we also show that the dependency of α on n is naturally explained if the scale-free nature of human daily operations holds for any n.

Recently, the tourism industry has developed remarkably. Marketing for revitalizing the tourism market has attracted intense attention. To perform effective marketing, analyzing attributes such as gender, age, and residential areas of visitors is a fundamentally important approach because it is possible to present an appropriate advertisement to each user considering user attributes. As described in this paper, we propose a method to estimate user attributes based on geographical information annotated to contents posted by users in social media posts. Attributes of people visiting a specific area might be biased, such as "men visit Shimbashi frequently" and "women often visit Harajuku." Our approach assumes that "people with a certain attribute often visit a certain area" and that "such areas differ depending on attributes." Based on those assumptions, we create feature vectors based on geographical information related to social media sites. Furthermore, we propose a method to estimate user attributes with feature vectors using machine learning. As described in this paper, we specifically examine estimation of user gender. Our experiments demonstrated evaluation of the efficiency of gender estimation using the proposed method from a dataset obtained from Twitter and Flickr.

Sustainable social development requires an advanced system of digitalization that can support reasonable and effective operation of devices in the real world. Many devices have features that change their conditions depending on the surrounding environments and their age. A key digital technology for the continuous change is a real-time analysis of data gathered in high volume and at high frequency from sensors attached to devices. DBSCAN is a clustering algorithm that can feasibly obtain insights into device conditions from real-world data. The challenge, however, is to update the clustering results quickly in response to high-volume and high-frequency data including changing device conditions. We propose the Re-DBSCAN algorithm for real-time cluster updating in response to changing device conditions. Re-DBSCAN updates hyperparameters from the data distribution affected by device conditions and refines recalculation of data that affect the cluster region. This refining process minimizes the calculation costs for updating cluster regions. Through evaluation, Cluster updating in 0.24 s was verified for 2.2 million power grid data with a reporting rate of one datum per second. This result indicates that Re-DBSCAN algorithm can classify high-frequency data continuously and that it provides insights into operations that are appropriate for the device conditions.

This paper proposes a method to estimate the posterior distribution of a Boltzmann Machine. Due to high feature extraction ability, a Boltzmann Machine is often used for both of supervised and unsupervised learning. It is expected to be suitable for multimodal data because of its bi-directional connection property. However, it needs a sampling method to estimate the posterior distribution, which becomes a problem during an inference period because of the computation time and instability. Therefore, it is usually converted to feedforward Neural Networks, which means to lose its bi-directional property. To deal with these problems, this paper proposes a method to estimate the posterior distribution of the Boltzmann Machine fast and stably without converting it to feedforward Neural Networks. The key idea of the proposed method is to estimate the posterior distribution using a Simulated Annealing on non-uniform temperature distribution. The advantage of the proposed method against normal sampling method and conventional Simulated Annealing is shown through experiments with artificial dataset and MNIST. Furthermore, this paper also gives the mathematical analysis of Boltzmann Machine's behaviour with regard to temperature distribution.

In this paper, we analyze the changes in behavior of travelers on the basis of the kind of travel and travel period using geotagged tweets. Travel can be classified into various types of purposes. For example, travelers visit Tokyo for various purposes like sightseeing or transiting to another area. In this paper, for a first experiment, we classified travelers visiting Tokyo in accordance with whether their main destination was Tokyo or not. Using the results, we discuss the differences in the behaviors of people on travels to and through Tokyo. We also used the same results to see whether the behavior of travelers changes depending on the length of travel and the day of the travel. For the second experiment, we compared the behaviors of travelers visiting Tokyo from two different regions (Kanto and Kyushu) to see whether place of residence affects behavior on travels of different lengths and on different days.

A place is one important factor that affects the relevance between two words. For example, in places where tourists can view Tokyo Tower and cherry blossoms at the same time, it is considered that these words are related. However, in other places, these words would not be considered related. In this research, we propose a method to extract the relationship between words in an area based on this hypothesis, using geotagged texts obtained from Twitter. To extract the relevant words of a word posted at each place, our approach uses propagation of words co-occurring with co-occurring words in texts. We apply Biased LexRank, adapted from PageRank, to a graph constructed from co-occurrence relationships in each area. We also determined the places where there are two characteristic objects based on the relationship of the words. We visualize and discuss such places based on the relationship, such as places where you can appreciate "Tokyo Tower" and "cherry blossom" together.

Shin Kanouchi, Mamoru Komachi, Naoaki Okazaki, Eiji Aramaki, Hiroshi Ishikawa. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

This paper applies a Tangible User Interface (TUI) for information curation using Near Field Communication (NFC) to an analogy game. The increase in text data is more remarkable in current IT society. Although those are usually accessed with using Graphical User Interface (GUI), users except experienced computer users have difficulty in reading and organizing data with GUI. In particular, information curation such as grouping related data / information and finding relationship among them is difficult. In order to solve this problem, an interface that can access text data intuitively is expected. We are developing a TUI based on NFC, by which a user can move and group text data in a similar manner when handling paper documents. As one of the promising applications of the proposed TUI, this paper focuses on creative thinking support, for which touching externalized thought by hand is expected to be effective. An experiment is conducted, in which test participants did an analogy game with using the proposed TUI. The experimental result shows experience of using the TUI affects the participants’ self-evaluation about idea creation.

Many photos shared on photo-sharing sites are annotated with tags and geo-tags. Some studies have demonstrated extraction of the geographical characterization which a tag represents as regions using those metadata. However, in some cases (e.g. coastline), a line is more suitable than a region as a geographical characterization of a tag. Therefore, we proposed a novel method to extract lines as a region as a geographical characterization. Results show that the distance of a coastline and many lines of our method is less than 500 m. Although, in this paper, only the coastline has been evaluated, this method is applicable to other tags as well.

A land cover map that represents the land surface of the earth is based primarily on analysis of remotely sensed images. However, the rate of concordance of existing land cover maps is not high. This lack of concordance results from a difference in classification methods and observation conditions of remotely sensed images. Also, conducting field surveys around the world is unrealistic. Therefore, we use ground level photographs from photo-sharing sites instead of field surveys. We propose a method to classify areas into land cover types using image features, geo-tags, titles and tags. Additionally, we create the land cover map using classified photographs. We evaluate the method using ground truth created manually. Results show that the accuracy of the proposed method is about 70 percent in New York.

Social media sites include many photos taken at various locations and times. In this paper, we propose a method to discover multiple hotspots, where many photos have been taken, using geo-tagging photos posted on social media sites. Additionally, we infer the range and shape of hotspots based on the deviation of the places where photos have been taken. We conducted experiments using image search results when we entered the search term "Rome" on Flickr. Our experimentally obtained results demonstrate that it is possible to discover multiple hotspots and to estimate the range and shape of each hotspot with high precision.

Decentralized XML databases are often used in electronic commerce (EC) business models such as e-brokers on the Web. To flexibly model such applications, we need a modeling language for EC business processes. To this end, we have adopted a query language approach and have designed a query language, called XBML, for decentralized XML databases used in EC businesses. The authors explain and validate the functionality of XBML by specifying e-broker business models and describe the implementation of the XBML server, focusing on the distributed query processing.

Nowadays, structured data such as sales and business forms are stored in data warehouses for decision makers to use. Further, unstructured data such as emails, HTML texts, images, videos, and office documents are increasingly accumulated in personal computer storage due to spread of mailing, WWW, and word processing. Such unstructured data, or what we call multimedia documents, are larger in volume than structured data and precious as corporate assets as well. So we need a document warehouse as a software framework where multimedia documents are analyzed and managed for corporate-wide information sharing and reuse like a data warehouse for structured data. We describe a prototype document warehouse system, which supports management of simple and compound documents, keyword-based and content-based retrieval, rule-based classification, SOM-based clustering, and XML data query and view rules.

New multimedia applications, such as digital libraries and document warehousing, require next-generation database systems enabling users to efficiently and flexibly develop and execute such networked applications. To this end, we focus on development of a database system which enables flexible and efficient acquisition, storage, access and retrieval, and distribution and presentation of large amounts of heterogeneous media data. We propose a multimedia database system for networked multimedia applications, based on an OODB model extended with agents. We describe an early prototype multimedia database system to verify the proposed approach, which supports multimedia scripts, keyword-based and content-based view retrieval with QoS control, self-organizing map-based clustering and WWW integration.

New multimedia applications have emerged on top of information infrastructures, such as on-demand services, digital libraries and museums, online shopping and document management, which require new databases. That is, next-generation database systems must enable users to efficiently and flexibly develop and execute such advanced multimedia applications. We focus on development of a database system which enables flexible and efficient acquisition, storage, access and retrieval, and distribution and presentation of large amounts of heterogeneous media data. We take an approach based on an object-oriented database, which is more suitable for the description of media structures and operations than a traditional relational database. We extend the object-oriented approach by providing temporal and spatial operators, and control of distributing computing and QOS (quality of service). In this paper, we describe a multimedia data model and its efficient implementation.

We have devised an object oriented DBMS called Jasmine for advanced applications. The paper describes the implementation, application and extension of Jasmine in detail. We focus on the impact of the design of its object oriented model and language on database implementation technology. We describe what part of traditional relational database technology we extend to handle object oriented features such as object identifiers, complex objects, class hierarchies, and methods. We introduce nested relations to efficiently store and access clustered complex objects. We use hash based methods to efficiently access nonclustered complex objects. We provide user defined functions directly evaluated on page buffers to efficiently process method invocation. We devise object oriented optimization of queries including class hierarchies, complex objects, and method invocation. We incorporate dedicated object buffering to allow efficient access to objects through object identifiers. We also describe nontrivial applications of Jasmine and discuss the validity of object oriented databases. We focus on a constraint management facility, which can be implemented by taking advantage of the extensibility of Jasmine. The facility includes constraint rules, called design goals, for automatic database population required by engineering applications. Finally, we describe a view facility for schema integration also needed by engineering applications in distributed environments. We focus on how we extend Jasmine to implement the facility.

Our objective is to resolve three types of heterogeneity-data model, database system, and semantic-in heterogeneous databases. The basic framework for this objective is realized in an autonomous decentralized database system, called Jasmine/M. Users describe their relational or object-oriented data models and schemas locally using the model primitives which Jasmine/M provides as a scripting language. Description using such primitives or scripts constitutes viewports, which resolve heterogeneity in data models and database systems at local sites. At relational viewports, both relational and object-oriented schemas defined at other sites are translated via scripts and viewed as relational schemas. Similarly at object-oriented viewports, any schema defined at other sites is viewed as object-oriented. Views are used to resolve semantic heterogeneity within viewports. The paper describes a stepwise approach to resolving the three types of heterogeneity and its implementation using active objects.>

New applications such as CAD, AI, and hypermedia require direct representation and flexible use of complex objects, behavioral knowledge, and multimedia data. To this end, we have devised a knowledge base management system called Jasmine. An object-oriented approach in a programming language also seems promising for use in Jasmine. Jasmine extends the current object-oriented approach and provides the following features. Our object model is based on functional data models and well-established set theory. Attributes or functions composing objects can represent both structural and behavioral knowledge. The object model can represent incomplete and generic knowledge. The model can support the basic storage and operations of multimedia data. The facets of attributes can flexibly represent constraints and triggers. The object manipulation language can support associative access of objects. The structural and behavioral knowledge can be uniformly treated to allow the user to specify complex object operations in a compact manner. The user-defined and system-defined attributes can be uniformly specified to ease user customization of the language. The classes and instances can be uniformly accessed. Incomplete knowledge can be flexibly accessed. The system has a layered architecture. Objects are stored in nested relations provided by extensive DBMS as a sublayer. User query of objects is compiled into relational operations such as select and join, which can be efficiently processed using hashing. The behavioral knowledge is compiled into predicate and manipulation function interfaces that can directly access tuples in a buffer.— Authors' Abstract

This paper presents a knowledge-based approach to design a portable natural language interface to database systems, called KID (Knowledge-based Interface to Database systems). To free the user from the need to know about query languages and databases, and to allow the user to access databases in natural language, KID incorporates a knowledge base called the world model (WM), which contains a domain model, linguistic knowledge, and database mapping knowledge. To transport KID to a new domain, the user has only to build a new WM for the domain; the WM is easy for the user to define and understand. KID's processing of the user's query using the WM is transparent enough to the user to enable easy debugging of the WM. Encapsulating all the domain-specific knowledge into the WM makes KID portable. This paper discusses the WM design and KID's knowledge-based processing of a query using the WM.