TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.     

A bibliometric perspective of learning analytics research landscape

ABSTRACT

Learning analytics is an emerging field of research, motivated by the wide spectrum of the available educational information that can be analysed to provide a data-driven decision about various learning problems. This study intends to examine the research landscape of learning analytics to deliver a comprehensive understanding of the research activities in this multidisciplinary field, using scientific literature from the Scopus database. An array of state-of-the-art bibliometric indices is deployed on 2811 procured publication datasets: publication counts, citation counts, co-authorship patterns, citation networks and term co-occurrence. The results indicate that the field of learning analytics appears to have been instantiated around 2011; thus, before this time period no significant research activity can be observed. The temporal evolution indicates that the terms ‘students’, ‘teachers’, ‘higher education institutions’ and ‘learning process’ appear to be the major components of the field. More recent trends in the field are the tools that tap into Big Data analytics and data mining techniques for more rational data-driven decision-making services. A future direction research depicts a need to integrate learning analytics research with multidisciplinary smart education and smart library services. The vision towards smart city research requires a meta-level of smart learning analytics value integration and policy-making.     

Nuclear desalination competitiveness in the western region of the Kingdom of Saudi Arabia

The competitiveness of nuclear desalination in the western region of Saudi Arabia was studied. A forecast of water demand between the years 2000 and 2025 was established as part of the input data required for the DEEP computer code. Several options of energy sources such as PWR (600 MWe), SPWR (160 MWth), PHWR (450 MWe), HR (200 MWth) and GT (125 MWe and 175 MWe) were investigated in conjunction with different desalination technologies such as RO, MSF, MED and the hybrid MED-RO. The total plant output, specific electricity investment cost, specific water investment cost, levelized power cost, average daily production, net saleable power and levelized water cost are presented for all cases. Two scenarios were investigated; the first assumed no interest and discount rates and the second assumed interest and discount rates equal to 4%, 8% and 12%. The first scenario assumed that the water utility continues under the control of the government while the second assumed that the water utility will be privatized.     

Measuring social media activity of scientific literature: an exhaustive comparison of scopus and novel altmetrics big data

This paper measures social media activities of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying area under the curve (AUC)—an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. It was found that Blog count was the most important factor in the field of Health Professions and Nursing as it increased the number of citations by 38.6%, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. The results of receiver operating characteristic show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC = 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics can be used to distinguish highly cited publications. The implications of this research are significant in many different directions. Firstly, they set the basis for a further investigation of altmetrics efficiency to predict publications impact and most significantly promote new insights for the measurement of research outcome dissemination over social media.     

An in-text citation classification predictive model for a scholarly search system

Scientometrics - We argue that citations in scholarly documents do not always perform equivalent functions or possess equal importance. To address this problem, we worked with a corpus of over...     

An adaptive doctor-recommender system

ABSTRACT

Recommender systems use machine-learning techniques to make predictions about resources. The medical field is one where much research is currently being conducted on recommender system utility. In the last few years, the amount of information available online that relates to healthcare has increased tremendously. Patients nowadays are more aware and look for answers to healthcare problems online. This has resulted in a dire need of an effective reliable online system to recommend the physician that is best suited to a particular patient in a limited time. In this article, a hybrid doctor-recommender system is proposed, by combining different recommendation approaches: content base, collaborative and demographic filtering to effectively tackle the issue of doctor recommendation. The proposed system addresses the issue of personalization through analysing patient's interest towards selecting a doctor. It uses a novel adoptive algorithm to construct a doctor's ranking function. Moreover, this ranking function is used to translate patients’ criteria for selecting a doctor into a numerical base rating, which will eventually be used in the recommendation of doctors. The system has been evaluated thoroughly, and result show that recommendations are reasonable and can fulfil patient's demand for reliable doctor's selection effectively.     

Intelligent Dynamic Inversion Controller Design for Ball and Beam System

The Ball and beam system (BBS) is an attractive laboratory experimental tool because of its inherent nonlinear and open-loop unstable properties. Designing an effective ball and beam system controller is a real challenge for researchers and engineers. In this paper, the control design technique is investigated by using Intelligent Dynamic Inversion (IDI) method for this nonlinear and unstable system. The proposed control law is an enhanced version of conventional Dynamic Inversion control incorporating an intelligent control element in it. The Moore-Penrose Generalized Inverse (MPGI) is used to invert the prescribed constraint dynamics to realize the baseline control law. A sliding mode-based intelligent control element is further augmented with the baseline control to enhance the robustness against uncertainties, nonlinearities, and external disturbances. The semi-global asymptotic stability of IDI control is guaranteed in the sense of Lyapunov. Numerical simulations and laboratory experiments are carried out on this ball and beam physical system to analyze the effectiveness of the controller. In addition to that, comparative analysis of RGDI control with classical Linear Quadratic Regulator and Fractional Order Controller are also presented on the experimental test bench.     

Hall effects on MHD chemically reacting flow of second grade fluid past a vertical porous plate

The scrutiny of the consequences of radiation-absorption, chemical reaction impacts on unsteady magnetohydrodynamics heat and mass transportation laminar flow of a gelatinous, electrical conducting by the heat generation or absorption second grade fluid embedded past a half-unlimited porous surface within a gyratory structure taking Hall effects into account have been discussed. The plate is assumed to as in the motion by the invariable velocity contained by the direction of fluid movement. A uniform magnetic field performed at perpendicular to the porous plate; this is engrossing the fluid with the suction velocity changing with definite instants of time. The corresponding dimensionless governing equations of current configuration are solved by making use of perturbation technique with reference to harmonic and nonharmonic idioms. The velocity, temperature, and concentration profiles are discussed and examined with references to various governing parameters in detail. The results are verified with the published work. The velocity components are increased with increasing permeability and Hall parameters. It is noticed that practical application of Hall effects is as, washing machine dial to select the type of washing moves very smoothly. This problem has also applications in biomedical and aerospace engineering.     

Introducing the ‘alt-index’ for measuring the social visibility of scientific research

Scientometrics - Knowing driving factors and understanding researcher behaviors from the dynamics of collaborations over time offer some insights, i.e. help funding agencies in designing research...     

Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion

This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion (AGDI) to track the position of a Linear Flexible Joint Cart (LFJC) system along with vibration suppression of the flexible joint. The proposed AGDI control law will be comprised of two control elements. The baseline (continuous) control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives. The control law is realized by inverting the prescribed dynamics using dynamically scaled Moore-Penrose generalized inversion. To boost the robust attributes against system nonlinearities, parametric uncertainties and external perturbations, a discontinuous control law will be augmented which is based on the concept of sliding mode principle. In discontinuous control law, the sliding mode gain is made adaptive in order to achieve improved tracking performance and chattering reduction. The closed-loop stability of resultant control law is established by introducing a positive define Lyapunov candidate function such that semi-global asymptotic attitude tracking of LFJC system is guaranteed. Rigorous computer simulations followed by experimental investigation will be performed on Quanser's LFJC system to authenticate the feasibility of proposed control approach for its application to real world problems.