ON RELEVANCE FILTERING FOR REAL-TIME TWEET SUMMARIZATION

Real-time tweet summarization systems (RTS) require mechanisms for capturing relevant tweets, identifying novel tweets, and capturing timely tweets. In this thesis, we tackle the RTS problem with a main focus on the relevance filtering. We experimented with different traditional retrieval models. Additionally, we propose two extensions to alleviate the sparsity and topic drift challenges that affect the relevance filtering. For the sparsity, we propose leveraging word embeddings in Vector Space model (VSM) term weighting to empower the system to use semantic similarity alongside the lexical matching. To mitigate the effect of topic drift, we exploit explicit relevance feedback to enhance profile representation to cope with its development in the stream over time. We conducted extensive experiments over three standard English TREC test collections that were built specifically for RTS. Although the extensions do not generally exhibit better performance, they are comparable to the baselines used. Moreover, we extended an event detection Arabic tweets test collection, called EveTAR, to support tasks that require novelty in the system's output. We collected novelty judgments using in-house annotators and used the collection to test our RTS system. We report preliminary results on EveTAR using different models of the RTS system.This work was made possible by NPRP grants # NPRP 7-1313-1-245 and # NPRP 7-1330-2-483 from the Qatar National Research Fund (a member of Qatar Foundation)

Nanoparticle Enhanced Radiotherapy

Nanoparticles have been shown to create a localised increase in dose deposition when combined with ionising radiation. Although this has been shown in the literature, there are several factors that can alter the level of enhancement, which need to be investigated before translating the use of nanoparticles for clinical treatments. This thesis aims to investigate three different aspects of this effect: (i) effect of nanoparticles when combined with proton therapy, (ii) study the combined effect of nanoparticle material, size and beam energy with photon irradiation, (iii) consider the biological impact with different cell lines, nanoparticle parameters and radiation types. To consider the effect of nanoparticles with protons, Monte Carlo simulations were developed to model the effects of nanoparticle concentrations. The use of nanoparticles at clinically relevant concentrations was shown to cause an effect on the Bragg peak, where changes were quantified in the model and validated experimentally. Both simulation and experiment demonstrated a shift in the distal edge of the Bragg peak, with a simulated shift of 4.5 mm compared to a measured shift of 2.2 mm with a beam of 226 MeV protons. To study the combined effect, another model was developed, studying the effect on dose deposition around a single nanoparticle with photon irradiation. Here the geometry could be altered such that the nanoparticle size and material were studied, as well as the effect of different incident beam energies. These simulations considered the effects on multiple scales to determine the extent of the enhancement, where it is then possible to inform where nanoparticles need to be localised to within a cell to observe the most beneficial effect. The highest level of enhancement was found with 2 nm gold nanoparticles and 90 keV photons. Finally to investigate the biological impact, an in vitro model was used with different cell lines, nanoparticles and radiation types, to gain an understanding of the biological effects. This was able to show differences in cell survival when comparing different cell lines, with different levels of radiosensitivity. As well as this, differences in DNA damage were shown when comparing X-ray radiotherapy and proton therapy. In terms of enhancement, gold nanoparticles were shown to be more effective with MCF-7 cells, whereas gadolinium based nanoparticles caused more cell kill for U87 cells

Response of carbon fiber reinforced polymers strengthened beams to elevated temperature

The use of carbon fiber reinforced polymers (CFRP) into the repair and retrofitting of concrete structures has been growing exponentially over the past two decades worldwide. The composite offers a superior strength- to- weight ratio as well as good durability in various service environments. The proper implementation of CFRP system involves a clean concrete surface, a powerful adhesive, such as epoxy resins together with compatible CFRP. However, one of the limiting factors towards the widespread of CFRP systems is attributed to its low resistance to elevated temperature and fire. Hence, efforts have been exerted to better understand and quantify this negative effect and to provide external protection for the system in order to alleviate the negative of impact of elevated temperature. This study focuses on assessing the impact of elevated temperature on the flexural strength of externally bonded CFRP with and without protection. Two sets of plain concrete beams have been prepared without protection and with a ready-to-use cementitious protective. All beams were subjected to temperature degrees of 70, 120 and 180 °C for 1, 2, 4 and 8 hours in a furnace. The flexural strength and mode of failure have been assessed for each set. The results of this work demonstrate the CFRP strengthened beams experienced a drastic loss in strength upon exposure to elevated temperature. The extent of the drop in strength varied according to degree of exposure as well as duration. On the whole, CFRP unprotected beams were able to restore 40% of the flexural strength at 70 °C, while the CFRP strengthened protected beams restored 20% of the flexural strength of the CFRP strengthened beams. At exposure of 120 °C the CFRP strengthened beams showed increase in the flexural strength of 40% over unstrengthened unprotected beams. The CFRP strengthened protected beams surpassed the flexural strength of the CFRP strengthened beams at 120 °C by 20%. At exposure of 180 °C, the CFRP strengthened protected and unprotected beams failed to restore the lost flexural strength for the four and eight hours of exposure. This was followed by the appearance of the normal flexural crack on all the beams. Yet, the separation of the CFRP laminates from the concrete surface were noticed only at exposure to temperatures of 120 and 180 °C. The preliminary cost of the CFRP strengthened unprotected was estimated as 90% higher than the unstrengthened unprotected beams and the CFRP strengthened protected assessed as 16% higher than the CFRP strengthened unprotected. The results unveiled the ability of the CFRP strengthened beams to enhance the flexural strength upon exposure to elevated temperature along with the ability of the fire protection system to further improve this strength. Future work should be resumed to investigate wider sets of composites, various temperatures schemes, long term properties as well as applying the system to steel reinforced beams. It is also recommended to investigate the cooling effect on the performance of the strengthened and protected beam

A meta-analysis of the effect of bimodal subtitling on vocabulary learning among adult EFL learners

oai:ojs2.focusonelt.com:article/1A meta-analysis is conducted to investigate the impact of English subtitling on EFL learners’ vocabulary improvement. This study is done by collecting and analyzing previous research investigated on the effect of bimodal subtitles on vocabulary learning among EFL learners in different contexts and settings. The main point is to systemize the existing literature on bimodal subtitle as a topic in relation to vocabulary learning, and to compare the results of different studies in this respect.  Thus, second language development could be addressed through using bimodal subtitling as one effective teaching method for EFL learners. In an effort to investigate previous literature, a meta-analysis is developed to measure the overall effect size of the study and to guide English educators accordingly. Stata 14 is used for the analysis. The results extracted from the 10 papers found overall positive effect of bimodal subtitling on vocabulary learning among adult EFL learners

Spatiotemporal Big Data Analytics for Future Mobility

University of Minnesota Ph.D. dissertation. May 2019. Major: Computer Science. Advisor: Shashi Shekhar. 1 computer file (PDF); xii, 161 pages.Recent years have witnessed the explosion of spatiotemporal big data (e.g. GPS trajectories, vehicle engine measurements, remote sensing imagery, and geotagged tweets) which has a potential to transform our societies. Terabytes of earth observation data are collected every day from thousands of places across the world. Modern vehicles are increasingly equipped with rich sensors that measure hundreds of engine variables (e.g., emissions, fuel consumption, speed, etc) annotated with timestamps and location data for every second of the vehicle’s trip. According to reports by McKinsey and Cisco, leveraging such data is potentially worth hundreds of billions of dollars annually in fuel savings. Spatiotemporal big data are also enabling many modern technologies such as on-demand transportation (e.g. Uber, Lyft). Today, the on-demand economy attracts millions of consumers annually and over $50 billion in spending. Even more growth is expected with the emergence of self-driving cars. However, spatiotemporal big data are of volume, velocity, variety, and veracity that exceed the capability of common spatiotemporal data analytic techniques. My thesis investigates spatiotemporal big data analytics that address the volume and velocity challenges of spatiotemporal big data in the context of novel applications in transportation and engine science, future mobility, and the on-demand economy. The thesis proposes scalable algorithms for mining “Non-compliant Window Co-occurrence Patterns”, which allow the discovery of correlations in spatiotemporal big data with a large number of variables. Novel upper bounds were introduced for a statistical interest measure of association to efficiently prune uninteresting candidate patterns. Case studies with real world engine data demonstrated the ability of the proposed approaches to discover patterns which are of interest to engine scientists. To address the high velocity challenge, the thesis explored online optimization heuristics for matching supply and demand in an on-demand spatial service broker. The proposed algorithms maximize the matching size while also maintaining a balanced provider utilization to ensure robustness against variations in the supply-demand ratio and that providers do not drop out. Proposed algorithms were shown to outperform related work on multiple performance measures. In addition, the thesis proposed a scalable matching and scheduling algorithm for an on-demand pickup and delivery broker for moving consumers with multiple candidate delivery locations and time intervals. Extensive evaluation showed that the proposed approach yields significant computational savings without sacrificing the solution quality

Factors Predicting the Willingness of Parents to Have Their Children Vaccinated against COVID-19 Pandemic: A Cross-sectional Survey in Jordan

Background: COVID-19 vaccination has proven effective in controlling   the spread of corona viruses. However, many parents remain unwilling to have their children vaccinated.  Purposes: This study aims to investigate   the willingness on the part of Jordanian parents to have their children receive COVID-19 vaccines and to examine the predictors of this parental willingness. These predictors/variables include parents’ demographic variables, risk perception, and trust in health authorities and healthcare professionals. Methods: In September 2020 an online survey was used to generate a sample made up of parents residing in every region of the country using a proportional cluster protocol.   A self-reporting questionnaire was used to generate the data.    Results: A total of 1,252 parents participated in this study.   Analysis revealed that 25.5% of the parents were willing to vaccinate their children and that a further 25%, approximately, trusted what the health authorities had to say about the pandemic.  While 31.4% trusted healthcare professionals for caring for COVID-19 infected people. Finally, the results of the study showed that parental risk perception, trust, gender, and education were significant predictors of the parents’ willingness to vaccinate their children. Conclusion/Implications for future practice: Among Jordanian parents, the high prevalence of opposition to vaccinating children may be explained by such factors as risk perception of COVID-19, trust in health authorities and healthcare professionals, and demographics.  Health promotion initiatives are needed to provide parents with clear, accurate, and transparent information about the possible risks of COVID-19 infection among children and the vaccine’s benefits for both   children and   communities.  &nbsp

Accounting Knowledge as One of the most important factors affecting the pricing of E-Banking Services – A field Study in Jordanian banks

The study aimed to examine the effect of accounting knowledge on  pricing the E-banking services  through studying the accounting system , constituents and role of cost accounting in  pricing services. In addition, it aimed at identifying the role of accounting knowledge of  information technology risks ( the cost and loss of security and protection ) in pricing E-banking services. Furthermore, this study sought to identify  the most important difficulties facing  E-banking  service pricing's process. The study found that accounting knowledge contributes highly to provide the appropriate data for making the decisions of E- banking service pricing in the Jordanian commercial banks through providing data pertaining to the cost of developing and operating E-services. It also showed that accounting knowledge of information technology risks affects  greatly the decisions of pricing taken by the Jordanian commercial banks through identifying the losses resulted by information technology risks as well as the cost of providing internal control standards. Jordanian commercial bank face obstacles linked to accounting system; these hinder  the pricing of  E- banking services on scientific bases. Key words:  Accounting system, pricing E-services , commercial banks , Islamic bank

The Effectiveness of Using PQ4R Strategy in Teaching Reading Comprehension in Arabic Language Subject among Ninth Grade Students’ Achievement in Jordan

The study aimed at recognizing the effective of using (PQ4R) Strategy in Teaching comprehension Reading in Arabic Subject among Ninth Basic grade Students achievement in Jordan. To achieve this aim, a survey was used. The sample consisted of (104) male and female students distributed as (52) male student and (52) female student, chosen randomly in two experimental groups and two control groups consisted of (26) male students and (26) female students in each one. An achievement test for reading comprehension was used as an instrument for the study.The results indicated that there are statistical significant differences in favor of the experimental group which used (PQ4R). The results also showed that there are significant differences in favor of female students. The study recommended that (PQ4R) strategy should be adopted as an effective teaching method

SHORT-TERM MEMORY PROCESSES IN CHILDREN WITH LEARNING DISABILITIES

The study evaluates the extent of online teaching among instructors at the selected universities in the Philippines. The study utilized 120 faculty members from the private and state universities in the Philippines. Participants of this study were classified according to gender, type of residence, number of years in teaching, highest educational attainment, and department. The researchers used quantitative and qualitative research designs. The quantitative research design utilized a Likert -type data instrument and statistical tools used were mean, t-test, and Analysis of Variance (ANOVA). The study was submitted and evaluated for its contents by the experts in the qualitative method. Moreover, qualitative data and information will be gathered through the interview to be conducted by the researchers themselves. The qualitative design process was guided by the qualitative and qualitative experts and was based on a qualitative framework by Creswell (2012). Results revealed that online teaching using JEL was "very high." The challenges derived from the qualitative views and statements experienced by the respondents were the following: (1) interrupted and unstable signal, (2) technical issues in the middle of using, (3) unpreparedness of the respondents as they shared that they need more training and hands-on, and (4) frequent maintenance of JEL

LOCATION MENTION PREDICTION FROM DISASTER TWEETS

While utilizing Twitter data for crisis management is of interest to different response authorities, a critical challenge that hinders the utilization of such data is the scarcity of automated tools that extract and resolve geolocation information. This dissertation focuses on the Location Mention Prediction (LMP) problem that consists of Location Mention Recognition (LMR) and Location Mention Disambiguation (LMD) tasks. Our work contributes to studying two main factors that influence the robustness of LMP systems: (i) the dataset used to train the model, and (ii) the learning model. As for the training dataset, we study the best training and evaluation strategies to exploit existing datasets and tools at the onset of disaster events. We emphasize that the size of training data matters and recommend considering the data domain, the disaster domain, and geographical proximity when training LMR models. We further construct the public IDRISI datasets, the largest to date English and first Arabic datasets for the LMP tasks. Rigorous analysis and experiments show that the IDRISI datasets are diverse, and domain and geographically generalizable, compared to existing datasets. As for the learning models, the LMP tasks are understudied in the disaster management domain. To address this, we reformulate the LMR and LMD modeling and evaluation to better suit the requirements of the response authorities. Moreover, we introduce competitive and state-of-the-art LMR and LMD models that are compared against a representative set of baselines for both Arabic and English languages