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Towards an Analytical Probe for Twitter Information Flow Micro-structure

Towards an Analytical Probe for Twitter Information Flow Mic...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Monaheng Ramokhoro Pheello Maboea Tresia Holtzhausen Phumlani Nhlanganiso Khoza Solidaridad South Africa Johannesburg South Africa Amazon Web Services Cape Town South Africa Nelson Mandela University Port Elizabeth South Africa Scilinx Research School of Computer Science & Applied Mathematics University of the Witwatersrand Johannesburg South Africa

This article reviews the concepts presented with the conceptual framework of the network society, both in historical and present considerations, and extends them towards creating an analytical probe for information flow micro-structure on Twitter. By considering the effects of societal interconnectedness as it relates to individuals, the philosophical foundations of the network society are predicated on network interconnectedness that goes beyond institutional integration. Although the manifestations are progressively becoming apparent, the literature is still largely lacking in empirical results to facilitate further theoretical developments. Specifically; we present arguments that support the notion that society is becoming highly interconnected, and that these developments warrant study. As a technical contribution, we present the foundations for an analytical tool that is formulated from the joint application of machine learning and network science. Technically, we construct a multilayer network composed of: user interaction patterns, topics that characterize the discourse, and the entities that are referenced in the discourse. The multilayer network then forms the mathematical object on which analysis can be conducted, and allows us to study information flow micro-structure. The set of multilayer networks, that is generated by varying time, serves as a technical underpinning on which to characterize information flow micro-structure on Twitter. As an overarching theme, we argue that developing an understanding of such information flows is of significance to: commercial entities, governments, and the general populace.

关键词： Social networking (online) Blogs Sociology Government Machine learning Tools Nonhomogeneous media

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Faculty Orientation Online Tool for First Year science Students: Transitioning for Success

Faculty Orientation Online Tool for First Year Science Stude...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Kumar, Bijeta Sharma, Bibhya Prasad, Ajendra University of South Pacific Faculty of Science Technology and Environment Suva Fiji

ISBN: (纸本)9781728163031

This paper investigates the design implementation of a discipline specific online orientation tool for new students of higher education. Many incoming students face difficulties because of the sudden change in the eduscape, the transition from high schools to higher education institutions made more difficult due to attributes such as different learning styles, lifestyles and traditional and cultural practices. In general, the lower pass rates in first year courses are especially noticeable for students who register late and those based in peripheral campuses and centres. This is also the case for many students who arrive to a new campus away from their countries for programmes not offered in their local campuses. This ICT enabled tool is designed to address the new students' anxieties and fears, and ease the adaptation to science Programmes in higher education. The tool also allows students to understand the dynamic environment, be informed about the academic and social issues and also have the ownership of the University. Based on the findings, the paper provides recommendations on the design, development and implementation of the Faculty Orientation Online tool (FOOT). © 2019 ieee.

关键词： Students

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Block-NID: A Conceptual Secure Blockchain Based National Identity Management System Model

Block-NID: A Conceptual Secure Blockchain Based National Ide...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Tonu, Md. Anisur Rahman Hridoy, Sharfuzzaman Ali, M. Ameer Azad, Salahuddin A. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Mirpur Dhaka1216 Bangladesh Central Queensland University ICT at School of Engineering Technology RockhamptonQLD4702 Australia

ISBN: (纸本)9781728163031

Blockchain technology is becoming more and more popular day-by-day with an escalating interest in numerous applications ranging from data management, financial services, cyber security, Internet of things (IoT) and food science to healthcare industry and brain research. There has been a remarkable interest witnessed in utilizing applications of blockchain for the delivery of safe and secure National Identity (ID) management. Also, blockchain can reform the traditional ID management practices to a more reliable means in terms of effective use and distribution through safe and secure data sharing. In the future blockchain could be a technology that may potentially help in personalized, authentic, and secure ID management by merging the entire real-time user data of a citizen and presenting it in an up-to-date secure setup. In this paper we review both the existing and latest developments in the field of ID card system by implementing blockchain as a model. We also discuss the applications of blockchain along with the challenges faced and future perspectives. © 2019 ieee.

关键词： Blockchain

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Non-Parametric Response Surface Analysis: Detecting Network Homotopy Variation

Non-Parametric Response Surface Analysis: Detecting Network ...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Phumlani Nhlanganiso Khoza Scilinx Research School of Computer Science & Applied Mathematics University of the Witwatersrand Johannesburg South Africa

Response surface analysis provides a mechanism to probe processes with problems that are either computationally impractical to solve directly, or have an intrinsic form of complexity (e.g., system-wide irreducible uncertainty, or emergent effects mediated by interaction dynamics). In virtually all fields of engineering, the method has been successfully applied to solve diverse problems. However; within the context of probing complex systems processes, we find considerably limited application. Additionally, the literature of non-parametric methods is not as well developed as its parametric counterpart. Given non-applicability of differentiability, or well-structured optimization, in some problem formulations; the problem of optimal configuration search becomes both conceptually and computationally challenging. We extend the surface response method to probe persistent features of filtrations constructed from network representations. We take an approach that treats networks as geometrical objects, and study their topological properties as a means to define topological equivalence classes via a continuous deformation transformation. Following developments in network based non-parametric profiling of response surfaces for multi-attribute heterogeneous value-type data; we present a procedure to automate network homotopy variation detection-for cases where the underlying kernel transformation is mediated by a network representation that is endowed with an intrinsic metric. We apply the Forman-Ricci curvature flow to detect the variation of homotopy as viewed from a filtration perspective. The resulting scheme enables empirical probing of optimal mean-variance properties of a yield quantity. The result is a fully non-parametric method that is not dependent on model parameter learning. We present this approach as a means to take into account the fundamental uncertainty that is associated with bandwidth selection when reflexive properties, or operational risk factors, are int

关键词： data analysis Uncertainty Filtration Response surface methodology Computational efficiency Probes Kernel

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Biomarker CA125 Feature engineering and Class Imbalance Learning Improves Ovarian Cancer Prediction

Biomarker CA125 Feature Engineering and Class Imbalance Lear...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Xiaoyan Yang Matloob Khushi Kamran Shaukat School of Computer Science The University of Sydney Sydney Australia School of Electrical Engineering and Computing The University of Newcastle Callaghan NSW Australia

Ovarian cancer is a fatal female reproductive cancer because it has no specific clinical manifestations and effective screening methods in the early stage. When it is discovered, it is already an advanced stage with a low cure rate. Therefore, it is of great significance to improve the diagnostic ability of early screening for ovarian cancer. In this study, we feature engineered CA125 by calculating the rate of change of CA125 in addition to selecting a few top-ranked important features from PLCO ovarian cancer dataset. The dataset was extremely imbalanced; the imbalance ratio (ratio of negative samples in the majority class to positive examples in the minority class) was 143.7. Twenty-three types of class-imbalanced learning methods were used in this study to improve the predictive ability of the model. We identified the decision tree method with the highest AUC value among nine classic classifiers (decision tree, AdaBoost, etc.) to build a model with the class imbalance method, and showed their comparison. We identified the decision tree using SVMSMOTE has the most robust predictive ability for ovarian cancer, with a PPV of 0.9041, AUC of 0.9532, the sensitivity of 0.7792, and specificity of 0.9982. The high PPV shown by the model selected in this study indicates that the true positive samples predicted by the model account for 90.41%. Compared with other studies, the PPV of this study increased by 81.3%. This study helps to improve the accuracy of early screening for ovarian cancer and makes the diagnosis of ovarian cancer more reliable.

关键词： Learning systems Sensitivity conferences Surgery Predictive models data engineering Decision trees

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Designing Mobile Health and Well-being Applications: A Value-oriented Approach

Designing Mobile Health and Well-being Applications: A Value...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Krassie Petrova Maria Elena Villapol Michelle Edwyn Shahper Richter School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand School of Māori Enterprise Business & Technology Western Institute of Technology at Taranaki New Plymouth New Zealand

ISBN: (纸本)9781665419741;9781665429917

Mobile health and well-being applications (MHWBAs) provide a means of enhancing the delivery of health care. Ensuring that value is delivered to health care customers (patients) can be a challenge as health initiatives are often supplier-centered and may not take into account patient expectations. In addition, stakeholders including clinicians, customers and MHWBA designers may be divided in their views about what features, and functionalities are required, and how much control patients should have. A close working collaboration between stakeholders may bring significant benefits. Therefore, service systems such as an MHWBA should provide value co-creation opportunities, and support patient engagement by creating a positive environment in which actors can network and communicate with empathy. This study proposes and validates a value-oriented framework for mobile services that can be used to derive MHWBA design guidelines. The framework is based on a three-dimensional typology of service value co-creation activities which considers the cocreation activity type, the dominant participating actor, and the dominant benefitting actor. The framework can be used to analyze the value co-creation potential of existing or proposed mobile apps in a range of service contexts. In this study, the framework was applied to the case of the Green Prescription (GRx) service and was used to identify a range of concrete value co-creating activities. In further work. these activities will underpin the design requirements for a GRx MHWBA, with relevant features supporting a value co-creation process that brings value to both GRx patients and GRx providers.

关键词： computer science Design methodology conferences Green products Collaboration Medical services data engineering

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The use of Automatic Speech Recognition in Education for Identifying Attitudes of the Speakers

The use of Automatic Speech Recognition in Education for Ide...

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computer science and data engineering (csde), ieee asia-pacific conference on

作者： Lomthandazo Matsane Ashwini Jadhav Ritesh Ajoodha School of Computer Science and Applied Mathematics DSI-NICIS National e-Science Postgraduate Teaching and Training Platform (NEPTTP) The University of the Witwatersrand Johannesburg South Africa Faculty of Science The University of the Witwatersrand Johannesburg South Africa School of Computer Science and Applied Mathematics The University of the Witwatersrand Johannesburg South Africa

State-of-the-art Automatic Speech Recognition (ASR) systems convert the spoken words into a corresponding text. One of the problems faced in ASR is that speakers have a different way of pronouncing words, and their accents are different from one speaker to another due to age, gender, nationality, rapidity of words, expressive form of the speaker. This paper uses two data sets, Surrey Audio-Visual Expressed Emotion (SAVEE) and The Ryerson Audio-Visual database of Emotional Speech and Song (RAVDESS) data sets to determine the effect of the tone in the learning environment by using the ASR and check which classifier is giving the best result. Feature such energy, Mel filter Central coefficients, energy etc. were extracted using jAudio and Waikato Environment for Knowledge Analysis (WEKA) data mining tools was used for classification. Classifiers called multilayer Perceptron (MLP) neural network model, Support Vector Machines (SVM), Simple Logistic Regression (SLR), K-Nearest Neighbour (K-NN) and Random Forests (RF) was used to obtain the results of the emotion state for the both data sets. The data sets used to train the classifiers are in ARFF format. The results show that SAVEE data sets overcomes RAVDESS data sets in overall emotion classification performance. The result shows that RF performed better than the other classifier. The performance of classification models is evaluated in WEKA using 10-fold cross validation. The presented study examines seven emotions- anger, happiness, sadness, fear, surprise, disgust and neutral.

关键词： Support vector machines Radio frequency Neural networks Tools Multilayer perceptrons Feature extraction data mining

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Detection of Skin Cancer Using Deep Neural Networks

Detection of Skin Cancer Using Deep Neural Networks

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Rahi, Md. Muzahidul Islam Khan, Farhan Tanvir Mahtab, Mohammad Tanvir Amanat Ullah, A.K.M. Alam, Md. Golam Rabiul Alam, Md. Ashraful BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728163031

Skin cancer is a huge issue which gets neglected very often. Sometimes the human eye is unable to precisely detect diseases from imaging data, in cases of doctor's manual inspection. In this age, we see the rise of use of deep learning methods in our daily life problem solving. Therefore, we develop an automated computerised system for detecting skin diseases using deep neural network algorithms. In the proposed model, we have used several neural network algorithms and analyse their performances to detect five major skin diseases and Figure out the best performing algorithm in terms of accuracy. CNN and by using Keras Sequential API, we have structured a new model to gainan accuracy of around 80%. Later, for comparison and also to increase accuracy we have used architectures that use pre-trained data. These transfer learning model includes VGG11, RESNET50 and DENSENET121. Among the algorithms used in the proposed models, resnet architecture achieve highest accuracy of 90%. © 2019 ieee.

关键词： Deep neural networks

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An Efficient Image Processing Technique for Brain Tumor Detection from MRI Images

An Efficient Image Processing Technique for Brain Tumor Dete...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Alam, Sadia Abdullah, Md. Khan, Fairoz Nower Ullah, A. K. M. Amanat Rahi, Md. Muzahidul Islam Alam, Dr. Md. Ashraful BRAC University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728163031

Brain tumor, a type of cancer, is caused by a genetic mutation of abnormal neuronal cells. However, it cannot be easily diagnosed and depends on the patient's symptoms which range from hemialgia, seizure, irregular vision, mental shift and many more. The symptoms may vary depending on the region of the tumor. Currently, Magnetic Resonance Imaging (MRI) scan is the foremost means for tumor detection as well as identifying the position and extent of the tumor for surgical procedure. However, the MRIs are needed to be manually checked by a professional to determine the results. We propose a system which is an effective image processing algorithm for detecting and recognizing the tumor from MRI images to obtain the image segmentation of brain bleeding more accurately, making immediate medical treatment possible. In this system, the same problem with different procedures and methods is tested to find the best conjunction through trial and error. Through the combination, the effectiveness and error ratio of the results is engrossed on. This approach is for early detection of brain tumors with high correctness which gives an adequate result by detecting brain tumor properly. © 2019 ieee.

关键词： Magnetic resonance imaging

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Using Ensemble Decision Tree Model to Predict Student Dropout in Computing science

Using Ensemble Decision Tree Model to Predict Student Dropou...

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2019 ieee asia-pacific conference on computer science and data engineering, csde 2019

作者： Naseem, Mohammed Chaudhary, Kaylash Sharma, Bibhya Lal, Aman Goel University of South Pacific School of Computing Information and Mathematical Sciences Suva Fiji

ISBN: (纸本)9781728163031

science, Technology, engineering and Mathematics (STEM) professionals play a key role in the development of an economy. STEM workers are critical thinkers as they contribute immensely by driving innovations. There is a high demand for professionals in the STEM fields but there is also a shortage of human resource in these areas. One way to reduce this problem is by identifying students who are at-risk of dropping out and then intervening with focused strategies that will ensure that these students remain in same the programme till graduation. Therefore, this research aims to use a data mining classification technique to identify students who are at-risk of dropping out from their Computing science (CS) degree programmes. The Random Forest (RF) decision tree algorithm is used to learn patterns from historical data about first-year undergraduate CS students who are enrolled in a tertiary institute in the South pacific. A number of factors are used which comprise of students demographic information, previous education background, financial information as well as data about students' academic interaction. Feature selection is performed to determine which factors have greater influence in students' decision in dropping out. Cross-validation techniques are used to ensure that the models are not over-fitted. Two models were built using a 5fold and 10-fold cross-validation and the results were compared using several measures of model performance. The results show that the factors corresponding to students' academic performance in a first-year programming course had the greatest impact student attrition in CS. © 2019 ieee.

关键词： Students

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