Physical Conditions in Circumstellar Gas surrounding SN 1987A 12 Years After Outburst

Two-dimensional spectra of Supernova 1987A were obtained on 1998 November 14-15 (4282 days after outburst) with the Space Telescope Imaging Spectrograph (STIS) on board the Hubble Space Telescope (HST). The slit sampled portions of the inner circumstellar ring at the east and west ansae as well as small sections of both the northern and southern outer rings. The temperature and density at these locations are estimated by nebular analysis of [N II], [O III], and [S II] emission line ratios, and with time-dependent photoionization/recombination models. The results from these two methods are mutually consistent. The electron density in the inner ring is ~ 4000 cm-3 for S II, with progressively lower densities for N II and O III. The electron temperatures determined from [N II] and [O III] line ratios are ~11,000 K and \~22,000 K, respectively. These results are consistent with evolutionary trends in the circumstellar gas from similar measurements at earlier epochs. We find that emission lines from the outer rings come from gas of lower density (n_e \la 2000 cm-3) than that which emits the same line in the inner ring. The N/O ratio appears to be the same in all three rings. Our results also suggest that the CNO abundances in the northern outer ring are the same as in the inner ring, contrary to earlier results of Panagia et al. (1996). Physical conditions in the southern outer ring are less certain because of poorer signal-to-noise data. The STIS spectra also reveal a weak Ha emission redshifted by ~100 km s-1 at p.a. 103\arcdeg that coincides with the recently discovered new regions that are brightening (Lawrence et al. 2000). This indicates that the shock interaction in the SE section of the inner ring commenced over a year before it became apparent in HST images.Comment: 25 pages, 6 figures, to appear in December 1, 2000 Astrophysical Journa

Automatic Human Joint Detection Using Microsoft Kinect

Automatic human joint detection has been used in many application nowadays. In this paper, we propose an approach to detect full body human joint method using depth and color image. The proposed solution is divided into 3 stage, which is image preprocess stage, distance transform stage, and anthropometric constraint analysis stage. The output of our solution is a stickman model with the same pose as in the given input image. Our implementation is done by using a Microsoft Kinect RGB and depth camera with 480x640 image resolution. The performance of this solution is demonstrated on several human posture

Self-adaptive Software Modeling Based on Contextual Requirements

The ability of self-adaptive software in responding to change is determined by contextual requirements, i.e. a requirement in capturing relevant context-atributes and modeling behavior for system adaptation. However, in most cases, modeling for self-adaptive software is does not take into consider the requirements evolution based on contextual requirements. This paper introduces an approach through requirements modeling languages directed to adaptation patterns to support requirements evolution. The model is prepared through contextual requirements approach that is integrated into MAPE-K (monitor, anayze, plan, execute - knowledge) patterns in goal-oriented requirements engineering. As an evaluation, the adaptation process is modeled for cleaner robot. The experimental results show that the requirements modeling process has been able to direct software into self-adaptive capability and meet the requirements evolution

Entity Recognition of User Profile on Twitter

Atribut trust scope sebagai atribut untuk menentukan tingkat kepercayaan sumber informasi, akan diisi dengan data yang terdapat pada user profile Twitter yang dikenal sebagai Bio Twitter. Hanya saja, data tersebut harus sesuai dengan karakteristik dan fungsi dari masing-masing atribut trust scope, seperti atribut pendidikan harus diisi dengan informasi yang berkaitan dengan latar belakang pendidikan dari pemilik profil tersebut. Untuk mendapatkan data yang sesuai dengan atribut, kami melakukan named entity recognition, yang merupakan salah satu kegiatan pada proses ekstraksi informasi. Oleh karena itu, paper ini menjelaskan hasil proses pengenalan entitas yang dilakukan terhadap data yang terdapat pada user profile. Perangkat lunak yang digunakan untuk mengenali data sebagai entitas adalah IndonesiaNetagger. IndonesiaNettagger, merupakan perangkat lunak untuk mengenali entitas yang ditulis dalam bahasa Indonesia. Kami melakukan penelitian dalam empat tahap, yaitu pengenalan entity dengan data Bio twitter yang asli,identifikasi kesalahan proses pengenalan, formalisasi data dan pengujian pengenalan entitas akhir. Hasil penelitian menunjukkan keberhasilan sebagai berikut; entitas Person dikenali dengan benar adalah sebesar 71% dari total data entitas yang tersedia, entitas Organization dikenali dengan benar sebasar 50%, entitas Position 20% dikenali denganbenar, dan 50% entitas Location dikenali dengan benar.Trust scope attribute as an attribute to determine the level of trust resources, will be filled with the data contained in the user profile of Twitter –one of social media- known as Bio Twitter. However, these data should be in accordance with the characteristics and functions of each attribute, such as education attribute must be filled in with the information relating to the educational background of the owner of the profile. To obtain the data corresponding to the trust scope attributes, we perform named entity recognition, which is one of the activities in the process of information extraction. Therefore, this paper describes the results of the entity recognition process performed on data contained in the user profile. Software used to recognize the data as an entity is IndonesiaNetagger, which is to perform entity recognition that written in Indonesian language. The software recognizes only five entities namely Person, Organization, Location, Position and Other. We carried out the research by conducting four stages namely entity recognition-with original data-Bio Twitter, error identification, formalizing data, and final test. The results show the success of entity recogniton as follow; Person entity is recognized correctly by 71% of the total data available, the entity Organization recognized correctly by 50%, 20% Position entity recognized correctly, and 50% recognized correctly as Location entity

Mengidentifikasi Persoalan Pada Proses Kolaborasi Belajar Berbasis Komputer

The limitation of distance, time, and resources become a cliche issues for education in the Age of Information and Communication Technology (ICT) at this time. Solutions to collaborate ICT into education or referred as Computer-Supported Collaborative Learning (CSCL) is a must to achieve the goals and equity of education. The issues that become a barrier to implement this CSCL, are the influence of motivation and cultural background of a person that affects to the successful of the collaborative learning activities. Motivation is the driving forces for someone to do or not to do something that his presence is always up and down or even exist or not. Moreover when cultural background of participants influenced the collaboration of learning especialy when they are not used to it. Therefore we need a CSCL model to build a conducives learning environment to sharing motivation among the participants of learning collaboration to achive the goals

Determining Trust Scope Attributes Using Goodness of Fit Test: A Survey

Indonesian, as one of the countries with high number of internet users has the potential to serve as the place with great information resources. However, these resources must be accompanied by the availability of dependable information. Information trustworthiness can be obtained by assessing the confidence level (trust) of the source of information. This can be determined by using trust scope attributes. Hence, in this study, we intended to establish the trust scope attributes by means of utilizing the ones contained in the User Profile provided by social media; in this case Facebook, Google+, Twitter, and Linkedin. We carried out the research by conducting four stages namely data collection, attributes grouping, attribute selection, and surveys. A survey was then distributed to 257 randomly selected respondents (divided into two clusters: civilians and military officers) to seek for their opinions in terms of what attributes were considered to be crucial in defining the believability of an information source. Chi-square Goodness of fit Test was conducted to compare observed data with data we would expect to obtain. The results of the research suggested that there was similar judgment in terms of dictating source of information trustworthiness chosen by the research participants with the attributes provided by trust scope category. In this research, both civilians and military officer clusters concurrently perceived that educational background was the most dependable attribute

Recommendation System on Knowledge Management System via OAI-PMH

Knowledge Management System (KMS) might be a transformation from Library Management System (LMS). This transformation is possible when we add several knowledge processes from KMS not available in LMS. When a KMS is a transformation from a LMS, functionalities derived from librarian system will be also available in KMS. One of this functionality is recommendation system, where patron may receive related and recommended reading, usually based on subject similarity between knowledge documents. However, recommendation is delivered from system directly to patron. There is no recommendation from one system directly to another. This research proposes a communication model to provide recommendation from one KMS to another using LMS interoperability language, OAI-PMH. Although there is no real implementation, authors  hope  that  this  model  may  become  a basic reference for a better one. The novelty of this research is how to accommodate recommendation system between KMSs if the interaction is via knowledge sharing protocol, which in this research is OAI-PMH

Automatic Human Joint Detection Using Microsoft Kinect

Automatic human joint detection has been used in many application nowadays. In this paper, we propose an approach to detect full body human joint method using depth and color image. The proposed solution is divided into 3 stage, which is image preprocess stage, distance transform stage, and anthropometric constraint analysis stage. The output of our solution is a stickman model with the same pose as in the given input image. Our implementation is done by using a Microsoft Kinect RGB and depth camera with 480x640 image resolution. The performance of this solution is demonstrated on several human posture