Identifying Relationships Among Sentences in Court Case Transcripts Using Discourse Relations

Case Law has a significant impact on the proceedings of legal cases. Therefore, the information that can be obtained from previous court cases is valuable to lawyers and other legal officials when performing their duties. This paper describes a methodology of applying discourse relations between sentences when processing text documents related to the legal domain. In this study, we developed a mechanism to classify the relationships that can be observed among sentences in transcripts of United States court cases. First, we defined relationship types that can be observed between sentences in court case transcripts. Then we classified pairs of sentences according to the relationship type by combining a machine learning model and a rule-based approach. The results obtained through our system were evaluated using human judges. To the best of our knowledge, this is the first study where discourse relationships between sentences have been used to determine relationships among sentences in legal court case transcripts.Comment: Conference: 2018 International Conference on Advances in ICT for Emerging Regions (ICTer

Optimal Bus Dispatching Policy Under Variable Demand Over Time And Route Length

The problems of scheduling and schedule co-ordination in bus operations have conflicting objectives related to user’s cost and operator’s cost. Passengers would like to have public bus services where there is less waiting time. Operators on the other hand would like to earn profit with lesser vehicle operating cost and a minimum number of buses. In developing countries where overloading of buses has long been considered necessary to ensure bus travel remains affordable to most socioeconomic groups, bus operators would in addition to larger headways, like to have higher load factors to increase revenue even though passengers would prefer less load factors as it provides a more comfortable journey. All these factors are further constrained by the fare levels, which may not make the revenue adequate to operate at the most economically optimal frequency and load factor. This paper considers a method that is an extension to Newell’s Optimal Dispatching Policy, to determine a fleet size and dispatching rate based on both operator’s cost and user’s cost including the disutility of standing, in order to arrive at a global cost optimum. It further investigates the financial viability of providing such a service and sets out a financial viability domain within which optimization can occur in practice. If the resulting dispatching rate is lower and does not fall within the domain of financial viability, then operating subsidies are considered necessary to maintain the economically optimum dispatching rate. This method to compute optimized dispatching rates is based on screen-line counts across given locations along a bus routes used in conjunction with a limited sample of on-board boarding and alighting surveys. Passenger revenues have been computed by a process of multiplication of the rationalized origin-destination matrix by the fare for distance travelled between the respective origins and destinations. Indicators have also been developed to determine average trip lengths for each route and average revenue per passenger together with the points of maximum capacity along the route. These indicators describe the nature of the demand that the bus route serves. The screen line counts provide the hourly variation in demand over a bus route throughout the day, which has been expressed in terms of a polynomial equation to determine the variation of demand over different time periods. By combining both functions, a composite function has been developed to determine; the daily passenger demand on a given route; the total revenue for operators, the average load factor and locations on the route where maximum loading occurs.Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne

Learning Analytics for E-Learning Content Recommendations

[EN] E-Learning systems have caused a rapid increase to the amount of learning content available on the web. It has become a time consuming and a daunting task for e-learners to find the relevant content that they should study. Existing e-learning technology lacks the automated capability to provide guidance for students to prioritize and engage in the most vital course content. The students who are unable to find out the most suitable resources, for their studies and the assignments, may waste most of their time on browsing and searching. Some of the “good-students” can indirectly act as good guides to other students. Average learners could follow the content adopted by good students in the process of learning. It is possible to capture the behaviour of “good-students” and expose it as a form of automated guiding. For thisto work it is important to be able to predict students who are going to be successful at the end of the course based on their performance during the early part of the course. This work demonstrates the use of data mining techniques on e-Learning data to enable “Good-students” to indirectly guide “Average-Students” to find the most relevant content on an e-Learning environment.Perera, A.; Tharsan, S. (2015). Learning Analytics for E-Learning Content Recommendations. En 1ST INTERNATIONAL CONFERENCE ON HIGHER EDUCATION ADVANCES (HEAD' 15). Editorial Universitat Politècnica de València. 121-126. https://doi.org/10.4995/HEAd15.2015.448OCS12112

Improving disease outbreak forecasting models for efficient targeting of public health resources

The forecasting models developed in this work can be utilized to effect better resource mobilisation for combatting dengue. For understanding human mobility in disease propagation, Mobile Network Big Data (MNBD) is a low cost data exhaust that provides rich insight into human mobility patterns, including better spatial and temporal granularity. Research focuses on the development of a human mobility model, using MNBD that can accurately depict aggregate human population movements in Sri Lanka, and from this determine which machine learning technique provides the best disease forecasting model