Reasoning about Knowledge and Belief: A Syntactical Treatment

The study of formal theories of agents has intensified over the last couple of decades, since such formalisms can be viewed as providing the specifications for building rational agents and multi-agent systems. Most of the proposed approaches are based upon the well-understood framework of modal logics and possible world semantics. Although intuitive and expressive, these approaches lack two properties that can be considered important to a rational agent's reasoning: quantification over the propositional attitudes, and self-referential statements. This paper presents an alternative framework which is different from those found in the literature in two ways: Firstly, a syntactical approach for the representation of the propositional attitudes is adopted. This involves the use of a truth predicate and syntactic modalities which are defined in terms of the truth predicate itself and corresponding modal operators. Secondly, an agent's information state includes both knowledge and beliefs. Independent modal operators for the two notions are introduced and based on them syntactic modalities are defined. Furthermore, the relation between knowledge and belief is thoroughly explored and three different connection axiomatisations for the modalities and the syntactic modalities are proposed and their properties investigated

Shopbots: A Syntactic Present, A Semantic Future

Despite high expectations, shopbots have yet to significantly facilitate a richer, more satisfying online shopping experience for users. By taking advantage of Semantic Web and Web services technologies, however, researchers can overcome current technological limitations and finally realize the shopbot's significant potential. © 2006 IEEE

Event detection from social network streams using frequent pattern mining with dynamic support values

Detecting events from streams of data is challenging due to the characteristics of such streams: data elements arrive in real-time and at high velocity, and the size of the streams is typically unbounded while it is not possible to backtrack over past data elements or maintain and review the entire history. Social networks are a good source for event identification as they generate huge amount of timely information representing what users are posting and discussing. In this research, we are developing methods for event detection from streams of data. More specifically, we are presenting a framework for detecting the daily occurring events or topics occurring in social network streams related to major events. Our approach utilizes the Frequent Pattern Mining method to detect the daily occurring frequent patterns, which are going to be our detected events. In addition, we propose a dynamic support definition method to replace the fixed given one. An experiment was run on two streams relating to two different major events to examine the detected events and to test our support definition method. The UK General Elections 2015 stream holds more than one million tweets, and the Greece Crisis 2015 stream contains more than 150k tweets. The detected events were evaluated against news headlines published the same day the event was found. The results revealed that the higher the streaming level (bigger window size), the more accurate the detected events. We also show that for too small sized windows, a more strict support definition method is needed to avoid detecting false or insignificant events

A Directional Change Based Trading Strategy with Dynamic Thresholds

Traders always seek a trading strategy that can increase and maximize their profits. However, given the current challenges in financial time-series streams – data elements (tick prices) arrive in real-time or almost real-time and at high velocity (at finer time scales) – it is difficult to identify and spot the best time and the most profitable price for trading. The Directional Change (DC) is an event-based approach for summarizing price movements based on a fixed given threshold value. An event in the DC approach is detected if the price change between two points satisfies the given threshold value. In this research, we aim to present a trading strategy based on the DC approach and a dynamic threshold to replace the fixed given one. We call this strategy, the Dynamic Threshold Trading Strategy (DT-TS). Thus, once a DC event is detected (a price change is identified) using the defined dynamic threshold, a trading action is triggered as prices continue to increase or decrease depending on the detected DC event. The trading action to be taken (buy or sell) depends on the previous day price transitions. An experiment was conducted on the FTSE 100 minute-by-minute prices stream to evaluate the DT-TS against different fixed threshold values and different trading strategies. Results showed that the DT-TS was the most profitable strategy among different fixed thresholds and all other examined trading strategies

CSM-349 - Benford's Law: An Empirical Investigation and a Novel Explanation

This report describes an investigation into Benford?s Law for the distribution of leading digits in real data sets. A large number of such data sets have been examined and it was found that only a small fraction of them conform to the law. Three classes of mathematical model of processes that might account for such a leading digit distribution have also been investigated. We found that based on the notion of taking the product of many random factors the most credible. This led to the identification of a class of lognormal distributions, those whose shape parameter exceeds 1, which satisfy Benford?s Law. This in turn led us to a novel explanation for the law: that it is fundamentally a consequence of the fact that many physical quantities cannot meaningfully take negative values. This enabled us to develop a simple set of rules for determining whether a given data set is likely to conform to Benford?s Law. Our explanation has an important advantage over previous attempts to account for the law: it also explains which data sets will not have logarithmically distributed leading digits. Some techniques for generating data that satisfy Benford?s law are described and the report concludes with a summary and a discussion of the practical implications

Dynamic user profiles for web personalisation

Web personalisation systems are used to enhance the user experience by providing tailor-made services based on the user’s interests and preferences which are typically stored in user profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the users’ changing behaviour. In this paper, we introduce a set of methods designed to capture and track user interests and maintain dynamic user profiles within a personalisation system. User interests are represented as ontological concepts which are constructed by mapping web pages visited by a user to a reference ontology and are subsequently used to learn short-term and long-term interests. A multi-agent system facilitates and coordinates the capture, storage, management and adaptation of user interests. We propose a search system that utilises our dynamic user profile to provide a personalised search experience. We present a series of experiments that show how our system can effectively model a dynamic user profile and is capable of learning and adapting to different user browsing behaviours

MENILAI PENINGKATAN KADAR HEMOGLOBIN PADA 1 JAM, 6 JAM, DAN 12 JAM PASCA TRANSFUSI DARAH PADA PASIEN TALASEMIA DI RSUD DR.ZAINOEL ABIDIN BANDA ACEH

Talasemia adalah kelainan darah bawaan yang ditandai oleh berkurangnya produksi satu atau lebih rantai globin. Pasien talasemia mengalami kekurangan hemoglobin sehat yang dibutuhkan tubuh untuk dapat teroksigenasi dengan baik. Transfusi darah merupakan tatalaksana utama pasien talasemia. Pemeriksaan kadar hemoglobin setelah transfusi merupakan pemeriksaan yang lazim, hanya saja sampai saat ini belum ada keseragaman waktu yang pasti untuk melakukan pemeriksaan tersebut, sehingga akan merugikan pasien dan menambah lamanya pasien di rumah sakit. Penelitian ini bertujuan untuk menilai peningkatan kadar haemoglobin pada 1 jam, 6 jam, dan 12 jam pasca transfusi darah pada pasien talasemia di RSUD Dr. Zainoel Abidin Banda Aceh. Penelitian ini merupakan penelitian analitik observasional dengan desain cross sectional. Penelitian ini dilakukan dengan memeriksa kadar haemoglobin pasien talasemia setelah 1 jam, 6 jam, dan 12 jam pasca transfusi pada pasien usia 1-18 tahun dan sesuai kriteria inklusi. Analisa statistik yang digunakan adalah uji paired t test. Hasil penelitian didapatkan 20 orang laki-laki dan 20 orang perempuan dan terbanyak pada kelompok umur 10-13 (35%), dengan nilai rata-rata kadar hemoglobin pada saat masuk sebesar 7,38 g/dL (95% CI). Pada 1 jam pasca transfusi, rata-rata kadar Hb pada pasien meningkat 8,97 g/dL (8,59-9,35 g/dL), pada 6 jam pasca transfusi, 8,95 g/dL (8,57-9,32 g/dL), pada 12 jam pasca transfusi meningkat sebesar 9,60 g/dL (9,17-10,03 g/dL). Penelitian ini menyimpulkan bahwa peningkatan kadar Hb secara signifikan terjadi pada 1 jam dan 12 jam pasca transfusi darah

Deploying self-organisation to improve task execution in a multi-agent systems

This paper discusses how the performance of a network of agents can be improved using a self-organisation technique. The multi-agent network performance can be improved by organizing the agents in clusters. Furthermore, principles of self-organisation can be used to create agent organisations triggered when some of the agents have high load. Hence, busy agents within the network may decide to create an organisation to receive extra support from other less busy agents in order to execute more tasks. The paper presents a simulation based on Repast Simphony that has been used to develop the proposed model and describes a set of experiments showing the performance of the system with and without the self-organisation technique

City Branding and Identity

AbstractThe well-being and satisfaction of citizens and visitors are strongly influenced by the image of a city or place, to which monumental or iconic buildings have a great contribution. The paper aims to discuss the influence of iconic architecture through creating identifiable images on Quality of life. The paper, firstly, puts forward very briefly the concepts of City Identity and Branding with an emphasis on city image, which is limited to the contribution of iconic buildings. Secondly, the paper discusses the contribution of iconic buildings through their meaning in terms of the image of the city to QOL