RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

Example based English to Bengali machine translation

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2008.Cataloged from PDF version of thesis report.Includes bibliographical references (page 31).In this thesis we propose a new architecture for example based English to Bengali machine translation. The proposed Example Based Machine Translation (EBMT) system has five steps: 1) Tagging 2) Parsing 3) Prepare the chunks of the sentence using sub-sentential EBMT 4) Using an efficient adapting scheme match the sentence rule 5) Translate from English to Bengali in the chunk and generate output with morphological analysis. We prepared our tag set for tagging the English sentence. Here we proposed an optimal adapting scheme for choosing sentence rule from the knowledge base of the EBMT system. Our current system can translate simple sentences. We also defined a way to translate a complex sentence using sub-sentential EBMT. As this system can add more rules in the knowledge base, eventually it can be use for general purpose English to Bengali machine translation.Khan Md. Anwarus SalamB. Computer Science and Engineering

Buddhist ‘Theory of Meaning’ (Apoha vāda) as Negative Meaning’

The paper concentrates on the most pressing question of Indian philosophy: what is the exact connotation of a word or what sort of entity helps us to identify the meaning of a word? The paper focuses on the clash between Realism (Nyāya) and Apoha vāda (Buddhist) regarding the debate whether the meaning of a word is particular/universal or both. The paper asserts that though Naiyāyikas and Mīmāṁsakas challenged against Buddhist Apoha vāda, yet they realized that to establish an opinion in support of a word that conceptually denotes a negative meaning first would be a very difficult task

Knowledge Expansion of a Statistical Machine Translation System using Morphological Resources

Translation capability of a Phrase-Based Statistical Machine Translation (PBSMT) system mostly depends on parallel data and phrases that are not present in the training data are not correctly translated. This paper describes a method that efficiently expands the existing knowledge of a PBSMT system without adding more parallel data but using external morphological resources. A set of new phrase associations is added to translation and reordering models; each of them corresponds to a morphological variation of the source/target/both phrases of an existing association. New associations are generated using a string similarity score based on morphosyntactic information. We tested our approach on En-Fr and Fr-En translations and results showed improvements of the performance in terms of automatic scores (BLEU and Meteor) and reduction of out-of-vocabulary (OOV) words. We believe that our knowledge expansion framework is generic and could be used to add different types of information to the model.JRC.G.2-Global security and crisis managemen

An Ontology based Text-to-Picture Multimedia m-Learning System

Multimedia Text-to-Picture is the process of building mental representation from words associated with images. From the research aspect, multimedia instructional message items are illustrations of material using words and pictures that are designed to promote user realization. Illustrations can be presented in a static form such as images, symbols, icons, figures, tables, charts, and maps; or in a dynamic form such as animation, or video clips. Due to the intuitiveness and vividness of visual illustration, many text to picture systems have been proposed in the literature like, Word2Image, Chat with Illustrations, and many others as discussed in the literature review chapter of this thesis. However, we found that some common limitations exist in these systems, especially for the presented images. In fact, the retrieved materials are not fully suitable for educational purposes. Many of them are not context-based and didn’t take into consideration the need of learners (i.e., general purpose images). Manually finding the required pedagogic images to illustrate educational content for learners is inefficient and requires huge efforts, which is a very challenging task. In addition, the available learning systems that mine text based on keywords or sentences selection provide incomplete pedagogic illustrations. This is because words and their semantically related terms are not considered during the process of finding illustrations. In this dissertation, we propose new approaches based on the semantic conceptual graph and semantically distributed weights to mine optimal illustrations that match Arabic text in the children’s story domain. We combine these approaches with best keywords and sentences selection algorithms, in order to improve the retrieval of images matching the Arabic text. Our findings show significant improvements in modelling Arabic vocabulary with the most meaningful images and best coverage of the domain in discourse. We also develop a mobile Text-to-Picture System that has two novel features, which are (1) a conceptual graph visualization (CGV) and (2) a visual illustrative assessment. The CGV shows the relationship between terms associated with a picture. It enables the learners to discover the semantic links between Arabic terms and improve their understanding of Arabic vocabulary. The assessment component allows the instructor to automatically follow up the performance of learners. Our experiments demonstrate the efficiency of our multimedia text-to-picture system in enhancing the learners’ knowledge and boost their comprehension of Arabic vocabulary

Energy Access in an Era of Low Carbon Transitions: Politicising Energy for Development Projects in India

This thesis examines the role of low carbon energy projects in widening energy access, progressing energy transitions and furthering development goals in rural India. Currently in development contexts, energy access and transitions are mobilised through micro energy projects like solar lanterns and micro-grids. The successes and failures of these projects are primarily assessed quantitatively – number of villages covered, number of households connected etc. However, this approach fails to understand how energy transitions projects perform in people’s everyday lives. It does not capture the reasons why they work for particular groups of people and not for others. To go beyond the quantitative understanding, this thesisfocuses on the micro-politics of everyday life that shape the effects energy transitions projects have on different groups of people. Itconsiders how power, politics and culture are vital for understanding the successes and failures of these projects. Theoretically the thesis conceives low carbon projects as low carbon assemblages to understand their fluid and contingent nature and the ways in which they are configured and reconfigured through relationships of power and everyday politics. Engaging with governmentality studies, it further considers how different, pre-existing and newly configured relationships of power conduct people’s conducts and sometimes lead to resistances for low carbon projects. The thesis critically examines three crucial aspects of low carbon energy projects by engaging with three key ideas – trusteeship, significances and resistances. Firstly, it explores how, by positioning themselves as trustees, particular actors seek to assemble and govern low carbon projects in order to achieve specific outcomes. Secondly, it investigates how, by focusing on particular significances of electricity, these interventions work to achieve particular development goals in different spaces of everyday life. Finally, it asks how and why different pressures and contestations emerge as everyday resistances in low carbon transitions. The thesis takes an ethnographic route of enquiry in order to examine energy in everyday life, using participant observations, interviews and photography. It explores two different low carbon projects – Lighting a Billion Lives (LaBL) solar lanterns project and Husk Power Systems (HPS) biomass micro-grids project – and contrasts them against the central grid and kerosene oil networks, in five villages in Bihar. Three key arguments emerge from the thesis. Firstly, electricity access should be understood as a spatially heterogeneous and temporally fluid idea. Its firm quantification and standardisation are problematic because electricity access is geographically and socially differentiated.It needs to be explored in an ethnographic manner, in which not onlyquestions of ‘how much’ but also of who, how and where are critical. Secondly, in energy and development projects, context matters. The society, culture, politics and economy of spaces in which projects are implemented mediate their impacts. Finally, the upkeep and maintenance of low carbon energy projects is not just about economies and supply chains of spare parts but also about cooperation and coordination between the project designers and users.Being able to fulfil people’s changing electricity requirements by building flexibility in the projects is critical to respond to these three issues. This will make projects more sustainable and increase people’s trust on low carbon projects leading to a convergence between energy access and energy transitions

Spell checkers and correctors : a unified treatment

The aim of this dissertation is to provide a unified treatment of various spell checkers and correctors. Firstly, the spell checking and correcting problems are formally described in mathematics in order to provide a better understanding of these tasks. An approach that is similar to the way in which denotational semantics used to describe programming languages is adopted. Secondly, the various attributes of existing spell checking and correcting techniques are discussed. Extensive studies on selected spell checking/correcting algorithms and packages are then performed. Lastly, an empirical investigation of various spell checking/correcting packages is presented. It provides a comparison and suggests a classification of these packages in terms of their functionalities, implementation strategies, and performance. The investigation was conducted on packages for spell checking and correcting in English as well as in Northern Sotho and Chinese. The classification provides a unified presentation of the strengths and weaknesses of the techniques studied in the research. The findings provide a better understanding of these techniques in order to assist in improving some existing spell checking/correcting applications and future spell checking/correcting package designs and implementations.Dissertation (MSc)--University of Pretoria, 2009.Computer Scienceunrestricte

Asia-Pacific Perspectives on Environmental Ethics

Papers from the Pacific islands, India, Bangladesh and elsewhere illustrate the ethical dilemma of environmental policy, sustainable development and the needs of communities to make a living

Formalization and modeling of human values for recipient sentiment prediction

Sentiment analysis is viewed generally as a text classification problem involving the prediction of the semantic orientation of a text. Much of the analysis has focused on the sentiment expressed in the sentence or by the writer but not the sentiment of the recipient. For example, the sentence “Housing costs have dropped significantly” might be assigned a negative classification by a sentiment analysis model, however humans from different works of life might express different sentiments. A landlord will likely express a negative sentiment while a renter might express a positive sentiment. Therefore, traditional sentiment analysis methods fail to capture the human centric aspects that motivate diverse sentiments. Additionally, attempts at predicting recipient sentiment have involved considerable human effort in the form of content analysis and empirical surveys, making the process expensive and time-consuming. Thus, the aim of this research is to develop a method of recipient sentiment analysis that is devoid of human input in the form of annotations or empirical surveys. The approach taken in this research involves applying a model of human values towards recipient sentiment prediction. The justification for this approach is based on the well-established principle that values influence human behaviour of which sentiment is a form. Therefore, if a persons’ values can be modelled quantitatively, when presented with some text, in theory the sentiment of the recipient can be predicted. This research proposes that the application of values in developing sentences is a generative process, that can be represented as a language model. A mechanism called Feature Switching (FS) that enables the determination of recipient’s sentiment from the value language model is also discussed. The resulting sentiment prediction model has an accuracy in the range of 72.2%-72.5% which is in and about the range of performance of existing systems which make use of content analysis and human annotated data