The F@ Framework of Designing Awareness Mechanisms in Instant Messaging

This paper presents our research on awareness support in Instant Messaging (IM). The paper starts with a brief overview of empirical study of IM, using an online survey and face-to-face interviews to identify user needs for awareness support. The study identified a need for supporting four aspects of awareness, awareness of multiple concurrent conversations, conversational awareness, presence awareness of a group conversation, and visibility of moment-to-moment listeners and viewers. Based on the empirical study and existing research on awareness, we have developed the F@ (read as fat) framework of awareness. F@ comprises of the abstract level and the concrete level. The former includes an in-depth description of various awareness aspects in IM, whilst the latter utilises temporal logic to formalise fundamental time-related awareness aspects. F@ helps developers gain a better understanding of awareness and thereby design usable mechanisms to support awareness. Applying F@, we have designed several mechanisms to support various aspect of awareness in IM

Perancangan Sistem Pengukur Jarak antara 2 Titik Wireless Xbee Pro Berdasarkan Nilai RSSI

Secara umum, pengukuran jarak berdasarkan waktu tempuh data memberikan nilai waktu tempuh yang didapat akan selalu berubah secara signifikan dan tidak dapat ditentukan Perubahannya. Oleh karena itu, dalam paper ini pengukuran dilakukan berdasarkan kekuatan sinyal yang diterima. Hal ini bertujuan untuk mengetahui tingkat keakurasian pengukuran dengan menggunakan wireless. Dalam penelitian ini, pertama adalah menganalisa teori tentang pengukuran jarak menggunakan RSSI dan pengaruhnya, kemudian merancang sistem untuk pengukuran. Nilai RSSI yang didapat diproses oleh mikrokontroller ATMega328P dan kemudian dikirim ke Visual Basic pada PC untuk dianalisa ke jarak. Di dalam visual basic digunakan model nilai rata-rata untuk memproses dan mengambil data akhir RSSI dari Xbee Pro ZB. Setelah penelitian dilakukan, diperoleh kesimpulan bahwa pengukuran kesalahan rata-rata adalah 2,35 meter dengan jarak sebenarnya 5-90 meter. Katakunci : RSSI , Visual basic, Xbee pro Z

A layered view model for XML with conceptual and logical extensions, and its applications

University of Technology, Sydney. Faculty of Information Technology.EXtensible Markup Language (XML) is becoming the dominant standard for storing, describing and interchanging data among various Enterprises Information Systems (EIS), web repositories and databases. With this increasing reliance on such self-describing, schema-based, semi-structured data language XML, there exists a need to model, design, and manipulate XML and associated semantics at a higher level of abstraction than at the instance level. However, existing OO conceptual modelling languages provide insufficient modelling constructs for utilizing XML structures, descriptions and constraints, and XML and associated schema languages lack the ability to provide higher levels of abstraction, such as conceptual models that are easily understood by humans. To this end, it is interesting to investigate conceptual and schema formalisms as a means of providing higher level semantics in the context of XML-related data modelling. In particular we note that there is a strong need to model views of XML repositories at the conceptual level. This is in contrast to the situation for views for the relational model which are generally defined at the implementation level. In this research, we use XML view and introduce the Layered View Model (LVM, for short), a declarative conceptual framework for specifying and defining views at a higher level of abstraction. The views in the LVM are specified using explicit conceptual, logical and instance level semantics and provide declarative transformation between these levels of abstraction. For such a task, an elaborated and enhanced OO based modelling and transformation methodology is employed. The LVM framework leads to a number of interesting problems that are studied in this research. First we address the issue of conceptualizing the notion of views: the clear separation of conceptual concerns from the implementation and data language concerns. Here, the LVM views are considered as first-class citizens of the conceptual model. Second we provide formal semantics and definitions to enforce representation, specification and definition of such views at the highest level of abstraction, the conceptual level. Third we address the issue of modelling and transformation of LVM views to the required level of abstraction, namely to the schema and instance levels. Finally, we apply LVM to real-world data modelling scenarios to develop other architectural frameworks in the domains such as dimensional XML data modelling, ontology views in the Semantic Web paradigm and modelling user-centred websites and web portals

IJA: An Efficient Algorithm for Query Processing in Sensor Networks

One of main features in sensor networks is the function that processes real time state information after gathering needed data from many domains. The component technologies consisting of each node called a sensor node that are including physical sensors, processors, actuators and power have advanced significantly over the last decade. Thanks to the advanced technology, over time sensor networks have been adopted in an all-round industry sensing physical phenomenon. However, sensor nodes in sensor networks are considerably constrained because with their energy and memory resources they have a very limited ability to process any information compared to conventional computer systems. Thus query processing over the nodes should be constrained because of their limitations. Due to the problems, the join operations in sensor networks are typically processed in a distributed manner over a set of nodes and have been studied. By way of example while simple queries, such as select and aggregate queries, in sensor networks have been addressed in the literature, the processing of join queries in sensor networks remains to be investigated. Therefore, in this paper, we propose and describe an Incremental Join Algorithm (IJA) in Sensor Networks to reduce the overhead caused by moving a join pair to the final join node or to minimize the communication cost that is the main consumer of the battery when processing the distributed queries in sensor networks environments. At the same time, the simulation result shows that the proposed IJA algorithm significantly reduces the number of bytes to be moved to join nodes compared to the popular synopsis join algorithm

Annotating Relationships between Multiple Mixed-media Digital Objects by Extending Annotea

Annotea provides an annotation protocol to support collaborative Semantic Web-based annotation of digital resources accessible through the Web. It provides a model whereby a user may attach supplementary information to a resource or part of a resource in the form of: either a simple textual comment; a hyperlink to another web page; a local file; or a semantic tag extracted from a formal ontology and controlled vocabulary. Hence, annotations can be used to attach subjective notes, comments, rankings, queries or tags to enable semantic reasoning across web resources. More recently tabbed Browsers and specific annotation tools, allow users to view several resources (e.g., images, video, audio, text, HTML, PDF) simultaneously in order to carry out side-by-side comparisons. In such scenarios, users frequently want to be able to create and annotate a link or relationship between two or more objects or between segments within those objects. For example, a user might want to create a link between a scene in an original film and the corresponding scene in a remake and attach an annotation to that link. Based on past experiences gained from implementing Annotea within different communities in order to enable knowledge capture, this paper describes and compares alternative ways in which the Annotea Schema may be extended for the purpose of annotating links between multiple resources (or segments of resources). It concludes by identifying and recommending an optimum approach which will enhance the power, flexibility and applicability of Annotea in many domains

Annotating Relationships between Multiple Mixed-media Digital Objects by Extending Annotea

Annotea provides an annotation protocol to support collaborative Semantic Web-based annotation of digital resources accessible through the Web. It provides a model whereby a user may attach supplementary information to a resource or part of a resource in the form of: either a simple textual comment; a hyperlink to another web page; a local file; or a semantic tag extracted from a formal ontology and controlled vocabulary. Hence, annotations can be used to attach subjective notes, comments, rankings, queries or tags to enable semantic reasoning across web resources. More recently tabbed Browsers and specific annotation tools, allow users to view several resources (e.g., images, video, audio, text, HTML, PDF) simultaneously in order to carry out side-by-side comparisons. In such scenarios, users frequently want to be able to create and annotate a link or relationship between two or more objects or between segments within those objects. For example, a user might want to create a link between a scene in an original film and the corresponding scene in a remake and attach an annotation to that link. Based on past experiences gained from implementing Annotea within different communities in order to enable knowledge capture, this paper describes and compares alternative ways in which the Annotea Schema may be extended for the purpose of annotating links between multiple resources (or segments of resources). It concludes by identifying and recommending an optimum approach which will enhance the power, flexibility and applicability of Annotea in many domains

A k-Nearest Neighbors Method for Classifying User Sessions in E-Commerce Scenario, Journal of Telecommunications and Information Technology, 2015, nr 3

This paper addresses the problem of classification of user sessions in an online store into two classes: buying sessions (during which a purchase confirmation occurs) and browsing sessions. As interactions connected with a purchase confirmation are typically completed at the end of user sessions, some information describing active sessions may be observed and used to assess the probability of making a purchase. The authors formulate the problem of predicting buying sessions in a Web store as a supervised classification problem where there are two target classes, connected with the fact of finalizing a purchase transaction in session or not, and a feature vector containing some variables describing user sessions. The presented approach uses the k-Nearest Neighbors (k-NN) classification. Based on historical data obtained from online bookstore log files a k-NN classifier was built and its efficiency was verified for different neighborhood sizes. A 11-NN classifier was the most effective both in terms of buying session predictions and overall predictions, achieving sensitivity of 87.5% and accuracy of 99.85%

A Swarm Intelligence inspired Autonomic Routing Scenario in Ubiquitous Sensor Networks

Autonomic computing has attracted large amount of attention as a novel computing paradigm in the past few years. In this paper, we explore the inherent accordance between autonomic computing and swarm intelligence. Then, we propose a swarm intelligence inspired autonomic routing scenario with a targeting application area in ubiquitous sensor network. This scenario covers most of the characteristics of autonomic computing. The working flow and steps of our SI inspired autonomic routing scenario are explained in detail together with some preliminary simulation results, such as the power consumption, delivery ratio etc