A Study into Math Document Classification using Deep Learning
Fatimah Alshamari and Abdou Youssef, Department of Computer Science, The George Washington University, Washington, D.C, USA
Document classification is a fundamental task for many applications, including document annotation, document understanding, and knowledge discovery. This is especially true in STEM fields where the growth rate of scientific publications is exponential, and where the need for document processing and understanding is essential to technological advancement. Classifying a new publication into a specific domain based on the content of the document is an expensive process in terms of cost and time. Therefore, there is a high demand for a reliable document classification system. In this paper, we focus on classification of mathematics documents, which consist of English text and mathematics formulas and symbols. The paper addresses two key questions. The first question is whether math-document classification performance is impacted by math expressions and symbols, either alone or in conjunction with the text contents of documents. Our investigations show that Text-Only embedding produces better classification results. The second question we address is the optimization of a deep learning (DL) model, the LSTM combined with one dimension CNN, for math document classification. We examine the model with several input representations, key design parameters and decision choices, and choices of the best input representation for math documents classification.
Math, document, classification, deep learning, LSTM
Proposed Model for Enhancing Retrieving Process in Big Data Management
Ayman E. Khedr1, Mohamed Attia Mohamed2, Abdulwahab Ali Almazroi3, 1University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Systems, Jeddah, Saudi Arabia, 2Future University in Egypt, Egypt, 3University of Jeddah, College of Computing and Information Technology at Khulais, Department of Information Technology, Jeddah, Saudi Arabia
Nowadays, operations of the Internet have a significant growth and size of data is increasing every second. Most of organizations and individuals were unaware of such data explosion because quantity of data is continuously increasing. Consequently, managing and controlling tools and methodologies of big data become critical aspect. One of the big issues that needed to be tackled when working with big data is how to manage data effectively. To address this issue, there are two main research directions exist. The first one is using big data frameworks like Hive and pig Latin while the other one is employing NoSQL data models like key-value, graph, column and document stores. In addition, unprecedented data volume and the complexity of managing data across complex multi- infrastructure only further exacerbate the problems. This paper reviews different representative techniques that treat with big data management challenges and finally, proposed a model for handling such issues.
Big data, NoSQL, Machine learning, JackHare, Hive
Blockchain-based Ticketing Solution for Collegiate Athletics
Zaki Zahed1, Matt Fitzgerald2, Ronald Sayles3, 1IT Engineering Department, Saudi Aramco, Dhahran, Saudi Arabia, 2TCP program, University of Colorado at Boulder, Boulder, Colorado, USA, 3TCP program, University of Colorado at Boulder, Boulder, Colorado, USA
This paper proposes an ecosystem for Blockchain-Based Ticketing Solution for Collegiate Athletics. Utilizing technologies such as digital ledgers paired with cryptography, this paper constructs a theoretical implementation of secure digital ticketing. Four components essential to operation are identified as: issuer, user, verifier and DID (Decentralized Identifiers). The proposed solution begins with an authenticated University user. Said user must grant the ticketing website access to the user's assigned University identifier through QR code/login. This initial handshake is signed with private keys of both the University and user which is confirmed by the ticketing website. A digital ticket to the event, signed with the website's private key, is then released to the user via smart contract. The smart contract is then stored by the ticketing website into the blockchain. Upon arrival at the event the user presents the digital ticket (QR code) signed by the website and user's private keys. By doing so, proof of identity through authenticated University identifier is confirmed while simultaneously executes the aforementioned smart contract. Once the ticket and respective signatures are verified through the University's QR code scanner, the user is granted access into the event and the ticket can no longer be reused/resold.
Blockchain, Digital Identity, Digital Ticketing, Collegiate Athletics
Genetic Algorithm for Exam Timetabling Problem-a Specific Case for Japanese University Final Presentation Timetabling
Jiawei LI and Tad Gonsalves, Department of Information & Communication Sciences. Faculty of Science and Technology, Sophia University, Tokyo, Japan
This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which could be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results and the comparison results prove that the initial population pre-training approach can improve the result quality.
Examination timetabling problem, Excel VBA, Direct chromosome representation, Genetic Algorithm Improvement
Geothermal Energy for Refrigeration and Air Conditioning, Sustainable Development, and the Environment
A.M. Omer* , Energy Research Institute (ERI), Nottingham NG7 4EU, United Kingdom
Geothermal heat pumps (GSHPs), or direct expansion (DX) ground source heat pumps, are a highly efficient renewable energy technology, which uses the earth, groundwater or surface water as a heat source when operating in heating mode or as a heat sink when operating in a cooling mode. It is receiving increasing interest because of its potential to decrease primary energy consumption and thus reduce emissions of the greenhouse gases (GHGs). The main concept of this technology is that it uses the lower temperature of the ground (approximately lessthan 32°C), which remains relatively stable throughout the year, to provide space heating, cooling and domestic hot water inside the building area. The main goal of this study was to stimulate the uptake of the GSHPs. Recent attempts to stimulate alternative energy sources for heating and cooling of buildings have emphasised the utilisation of the ambient energy from ground source and other renewable energy sources. The purpose of this study, however, was to examine the means of reducing of energy consumption in buildings, identifying GSHPs as an environmental friendly technology able to provide efficient utilisation of energy in the buildings sector, promoting the use of GSHPs applications as an optimum means of heating and cooling, and presenting typical applications and recent advances of the DX GSHPs. The study highlighted the potential energy saving that could be achieved through the use of ground energy sources. It also focused on the optimisation and improvement of the operation conditions of the heat cycle and performance of the DX GSHP. It is concluded that the direct expansion of the GSHP, combined with the ground heat exchanger in foundation piles and the seasonal thermal energy storage from solar thermal collectors, is extendable to more comprehensive applications.
Geothermal heat pumps, direct expansion, ground heat exchanger, heating and cooling