Miracle-dz/UniFAR-FTUs huggingface dataset:
Healthcare receivers’ acceptance of telecardiology in Malaysia

This quantitative study investigates acceptance towards implementation of telecardiology in Malaysia. The purpose of this study is to explore and understand the potential factors that could be the key elements in cultivating positive behaviour towards telecardiology adoption in Malaysia. Data was gathered by using survey method from 149 patients and publics who use internet service in their daily lives. The questionnaire was developed by integrating Technology Readiness Index (TRI), Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) which consists of 42 items. Descriptive statistics, factor analysis and one-way ANOVA were conducted to analyse the data. The analysis reveals that Malaysians generally hold a positive perception towards implementation of telecardiology with some reservations. Besides that, gender and income of the respondents were found to influence the variables in telecardiology readiness, hence suggesting these two variables be taken into considerations in the adoption of telecardiology. It is our hope that the result of this study provides some useful information for the policy makers and implementers to develop effective implementation strategies that could reduce users’ resistance and adoption barriers in telecardiology adoption.
cudaIFT: 180x Faster Image Foresting Transform for Waterpixel Estimation using CUDA

We propose a GPU-based version of the Image Foresting Transform by Seed Competition (IFT-SC) operator and instantiate it to produce compact watershed-based superpixels (Waterpixels). Superpixels are usually applied as a pre-processing step to reduce the amount of processed data to perform object segmentation. However, recent advances in image acquisition techniques can easily produce 3D images with billions of voxels in roughly 1 second, making the time necessary to compute Waterpixels using the CPU-version of the IFT-SC quickly escalate. We aim to address this fundamental issue, since the efficiency of the entire object segmentation methodology may be hindered by the initial process of estimating superpixels. We demonstrate that our CUDA-based version of the sequential IFT-SC operator can speed up computation by a factor of up to 180x for 2D images, with consistent optimum-path forests without requiring additional CPU post-processing.
On the Role of Central Individuals in Influence Propagation

Recently, the influence of individuals in complex networks received the attention of several fields of science. In the context of influence spreading, the understanding of the role and importance of each individual can be used to control the spread of memes. By considering centrality measures as defining factors of individual importance, this paper investigates the relationship between the importance of an individual and its role in the propagation of influence within and over a network. In order to do so, we used degree measures, betweenness centrality, closeness centrality, eigenvector centrality and clustering coefficient over four different real graphs. The Min-SEIS-Cluster model was employed in order to simulate the spread of memes, which involve cutting connections to minimize an epidemic. The results revealed a high correlation between individual importance and prominence on influence propagation, and the potential to utilize centrality measures to identify which connections should be cut off in specific application scenarios.
An Investigation of Multi-Language Age Classification from Voice

In this paper, we investigate the use of deep neural networks (DNN) for a multi-language age classification task using speaker’s voice. For this purpose, speech databases in two different languages are combined together to construct a multi-language database. Mel-frequency cepstral coefficients (MFCC) are extracted for each utterance. A Gaussian mixture model (GMM), a support vector machine (SVM) and a feed-forward deep neural network (DNN) systems are trained using the features. In the SVM and DNN methods, the GMM means are concatenated to obtain a GMM supervector. The supervectors are fed into the SVM and DNN for age classification. In the experiments, we observe that the multi-language training does not degrade the performance in the SVM and DNN methods when compared to the matched training where train and test languages are the same. On the other hand, the performance is degraded for the traditional GMM method. Additionally, the SVM and DNN significantly outperform the GMM in the multi-language train-test scenario. The absolute performance improvement with the SVM and DNN is approximately 12% and 7% for female and male speakers, respectively.
Deep Auto Encoder Model With Convolutional Text Networks for Video Recommendation

Collaborative filtering (CF) approach has been successfully used in recommender system (RS). Sparsity and cold start are two common phenomena in the CF algorithms nearly for each data set. Hence, these drawbacks of the classical CF algorithms have limited the recommendation performance. Deep learning theory is a very useful tool to mine the latent features in many scientific areas, such as image processing, video processing, and signal processing. In this paper, a novel deep learning-based recommendation model is introduced to solve the sparsity and cold start recommendation problems by mining the auxiliary data of users’ viewing behavior datasets (e.g., the user attribute features information and video item attribute features information) and to deeply mine the latent information and their correlations of the user features and item features. First of all, the user features and video item features are processed and deeply mined by the data preprocessing layer, embedding dense layer, convolution network layer, share layer, and the auto encoder layer of our proposed model. After that, the final predictive rating process is conducted in multi-layer perception by combining with the target rating vector data and the processed user and item feature data, which is deeply mined by the above-mentioned submodels of our proposed algorithm model. The extensive experiments have shown the benefits of the proposed algorithm in the measure of mean absolute error (MAE) and root mean square error (RMSE) compared with the state-of-the-art algorithms. Besides, the impact of choices of different components and parameters of our proposed algorithm model is also studied thoroughly.
THE METHODOLOGY OF DESIGN BY GEOMETRIC CODE IN TRADITIONAL ARCHITECTURE

The role of geometric code as a design generator in architecture design throughout history hadnot demonstrated in any specialized study. The most concern for this research understands howsome historical buildings may have been designed in the first place by the aid of geometry. Theresearch started a process of geometric decoding for these buildings beginning withunderstanding the geometric methodology of design which were used. Geometrical analyses ofvarious designs of historical buildings from Greek, Roman and Islamic architecture , arepresented to illustrate how design idea and the building design have been derived geometricallyby depending on the geometric code, which remain the backbone of the process of “designing”of all buildings, and it played the role of design generator which control all the design process.The outcome of research analysis were listed in a set of observations which show a certainrelationship between building design and geometry, arising from the use of geometric code as thecore of the process of architecture design, not only in these selected buildings but generally inthe architecture of the different historical periods. دور الشفرة الھندسية کمولد للتصميم المعمارى لم يتم تناولھ فى دراسة متخصصة على مدار التاريخ. يرکز ھذا البحث فىالمقام الاول على فھم کيف يتم انتاج التصميم المعمارى لبعض المبانى التاريخية بمساعدة علم الھندسة. يبدا البحث بفک الشفرةالھندسية لھذه المبانى من خلال فھم منھجية التصميم الھندسية التى استخدمت فى تصميم تلک المبانى. تم اجراء عملية تحليلھندسى للعديد من المبانى التاريخية من العمارة اليونانية والرومانية والإسلامية وشرح کيف تم صياغة الفکرة التصميميةوالتصميم المعمارى للمبنى بالاعتماد على الشفرة الھندسية والتى ظلت تمثل العمود الفقرى للعملية التصميمية للمبنى وتلعبدور المولد التصميمي الذى يسيطر على کل محاور العملية التصميمية. والنتائج النھائية لعملية التحليل الھندسى تم صياغتھ فىصورة مجموعة ملاحظات توضح العلاقة الوثيقة بين تصميم المبنى وعلم الھندسة والتى تظھر بوضوح من خلال استخدامالشفرة الھندسية کمحور أساسى لعملية تصميم المبانى ليس فقط فى ھذه العينة المختارة من المبانى ولکن عموما فى عمليةالتصميم المعمارى فى العديد من الحقب التاريخية المختلفة وبذلک تم صياغة منھجية للتصميم المعمارى باستخدام الشفرةالھندسية فى العمارة المعاصرة الحديثة. المختلفة.
Maximizing the Impact of Competitive Relation Based on Node diffusion Model

The problem of maximizing influence is to select a small number of nodes (seed nodes) in social networks so that they could maximize the spread of influence. In our paper, we have two contributions. First, we consider the impact of competition among products on nodes based on the Susceptible-Infected (SI) information diffusion model and improve it into a Rival Susceptible-Infected (RSI) information diffusion model. Second, the probability of influence between nodes is different based on different products under the same theme with competitive relations, and in the process of the node spreading one of the products, other products under the same theme have a competitive blocking effect on the node. So a new heuristic algorithm— Influence Reconstitution Algorithm (IRA)—is proposed to consider the impact of product competition on the nodes and the distance between the initial nodes. Our algorithm introduces the k-order core competition influence and coincidence rate P. Through the coincidence rate P, the influence of the initial node is reasonably controlled, and the most influential nodes are found in order. Our experiments based on real data sets shows that the IRA algorithm has better competitive effect than the existing heuristic algorithm in the competitive relational social network.
Geolocalization in Smart Environment

Nowadays, wireless indoor positioning systems have become very familiar, and widespread all over the world. They are successfully used in many applications including tracking objects e.g. Firemen who usually face life-threatening situations. Indoor positioning systems become critically convenient in such scenarios. This paper deals with the tracking of a group of firemen during their mission in order to have a real-time visibility of their coordinates. These firemen are armed by smart sensors and are, at the same time, active in a smart environment containing referenced nodes. This paper will propose two approaches: ‘Centralized Emission’, and ‘Broadcast Emission’ and will describe the proposed method to calculate the firemen’s coordinates.
Library data visualisation transformations: global patterns for research evaluation metrics in China

The new information environment in higher education libraries is shaped by an ever changing scholarly communication infrastructure and evolving cycles of research production, communication and evaluation. New services have emerged for research data analytics, based around strategic research intelligence. This paper focuses on library data visualisation transformations for research evaluation metrics in physical library spaces using physical hardware technologies and software, notably, in this study, CiteSpace and Google Fusion Table. This paper focuses on research evaluation metrics and the evolution of a data analytics service in China. Data-driven research intelligence is an emerging need for universities, including reporting to funding organisations. Deeper understandings of collaborative authorship and citation clusters can provide a comparative edge for strategy development. Leading universities need research data to analyse the performance at multiple levels. This paper focuses on data visualisation in the physical library using wall and table technologies. It showcases how library atrium spaces are transformed through the data visualisation of bibliometric data, where the data can be presented in ways that are beautiful, boring and functional. The paper will demonstrate how library spaces create opportunities for interaction, where researchers can experience data or interact and manipulate data to create new meaning, narratives and stories. The paper presentation will include video examples of data visualisation and the visualisation of data in physical library spaces, projecting a future for data visualisation in new library buildings on university campuses.
Collaborative Cloud and Edge Computing for Latency Minimization

By performing data processing at the network edge, mobile edge computing can effectively overcome the deficiencies of network congestion and long latency in cloud computing systems. To improve edge cloud efficiency with limited communication and computation capacities, we investigate the collaboration between cloud computing and edge computing, where the tasks of mobile devices can be partially processed at the edge node and at the cloud server. First, a joint communication and computation resource allocation problem is formulated to minimize the weighted-sum latency of all mobile devices. Then, the closed-form optimal task splitting strategy is derived as a function of the normalized backhaul communication capacity and the normalized cloud computation capacity. Some interesting and useful insights for the optimal task splitting strategy are also highlighted by analyzing four special scenarios. Based on this, we further transform the original joint communication and computation resource allocation problem into an equivalent convex optimization problem and obtain the closed-form computation resource allocation strategy by leveraging the convex optimization theory. Moreover, a necessary condition is also developed to judge whether a task should be processed at the corresponding edge node only, without offloading to the cloud server. Finally, simulation results confirm our theoretical analysis and demonstrate that the proposed collaborative cloud and edge computing scheme can evidently achieve a better delay performance than the conventional schemes.
Analysis of nonlinear subdivision and multi-scale transforms

Subdivision is a process of recursively refining discrete data using a set of subdivision rules to generate limits (curves, surfaces, height fields) with desirable properties such as continuity, smoothness, reproduction of shape features, and many more. The wide range of applications as well as the necessity of improving the performance of the existing algorithms lead to the invention of a great variety of subdivision schemes. In many cases, such as preserving the data shape, using normal meshes for better compression rates, removing heavily-tailed noise, working with manifold-valued data, linear multi-scale transforms give unsatisfactory results or cannot be applied at all, and nonlinear alternatives are necessary. There are still very few results about Lipschitz stability and Holder regularity of nonlinear subdivision schemes and the associated multi-scale transforms, which is a very active research field with many open problems, that is driven by both theory and applications. In this thesis we develop a general stability analysis of both univariate schemes and their associated multi-scale transforms in the nonlinear functional setting. We show that, unlike the linear setting, convergence and stability analysis are no longer equivalent, and we derive numerical criteria for the verification of each of them. We extend the univariate convergence and stability results to the multivariate regular setting via local approximation techniques. We establish a general theory for normal multi-scale transforms for curves, based on approximating prediction operators. We propose a globally-convergent normal multi-scale transform, and build an adaptive algorithm based on it that defines a well-posed transform with smooth limits and high detail decay rates. We investigate several extensions of the classical setup for normal multi-scale transforms, namely we use another subdivision operator to generate the normal directions, the combined action of two different subdivision operators for the prediction step, and nonlinear geometry-based predictors, respectively, and show that the properties of the normal multi-scale transforms improve when such extensions are considered.
Equivalence of Turn-Regularity and Complete Extensions

The aim of the two-dimensional compaction problem is to minimize the total edge length or the area of an orthogonal grid drawing. The coordinates of the vertices and the length of the edges can be altered while all angles and the shape of the drawing have to be preserved. The problem has been shown to be NP-hard. Two commonly used compaction methods are the turn-regularity approach by (Bridgeman et al., 2000) and the approach by (Klau and Mutzel, 1999) considering complete extensions. We formally prove that these approaches are equivalent, i. e. a face of an orthogonal representation is turn-regular if and only if there exists a unique complete extension for the segments bounding this face.
STATIC ENFORCEMENT OF TERMINATION-SENSITIVE NONINTERFERENCE USING THE C++ TEMPLATE TYPE SYSTEM

A side channel is an observable attribute of program execution other than explicit communication, e.g., power usage, execution time, or page fault patterns. A side-channel attack occurs when a malicious adversary observes program secrets through a side channel. This dissertation introduces Covert C++, a library which uses template metaprogramming to superimpose a security-type system on top of C++’s existing type system. Covert C++ enforces an information-flow policy that prevents secret data from influencing program control flow and memory access patterns, thus obviating side-channel leaks. Formally, Covert C++ can facilitate an extended definition of the classical noninterference property, broadened to also cover the dynamic execution property of memory-trace obliviousness. This solution does not require any modifications to the compiler, linker, or C++ standard. To verify that these security properties can be preserved by the compiler (i.e., by compiler optimizations), this dissertation introduces the Noninterference Verification Tool (NVT). The NVT employs a novel dynamic analysis technique which combines input fuzzing with dynamic memory tracing. Specifically, the NVT detects when secret data influences a program’s memory trace, i.e., the sequence of instruction fetches and data accesses. Moreover, the NVT signals when a program leaks secret data to a publicly-observable storage channel. The Covert C++ library and the NVT are two components of the broader Covert C++ toolchain. The toolchain also provides a collection of refactoring tools to interactively transform legacy C or C++ code into Covert C++ code. Finally, the dissertation introduces libOblivious, a library to facilitate high-performance memory-trace oblivious computation with Covert C++. STATIC ENFORCEMENT OF TERMINATION-SENSITIVE NONINTERFERENCE USING THE C++ TEMPLATE TYPE SYSTEM by Scott Constable B.A., Ithaca College, 2012 M.S., Syracuse University, 2014 Dissertation Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer and Information Science and Engineering Syracuse University December 2018 Copyright c © Scott Constable 2018 All Rights Reserved
A novel localization approach for underwater welding vehicles in spent fuel pools via attitude heading reference system and altimeters

In this article, a novel localization approach incorporating attitude and heading reference system and underwater altimeters is presented to accurately localize the underwater welding vehicles in spent fuel pools of the nuclear power stations. Different from the conventional underwater localization technologies, the presented localization approach is a more suitable approach in cases of confined structured water areas. Firstly, a multi-regions division localization algorithm is proposed for calculating the coordinate of the underwater welding vehicle through data from sensors. Also, considering the attitude errors of the underwater welding vehicle, the beam angle of the altimeters, and the boundary effects of cross-regions, an optimized multi-regions division localization algorithm is introduced for general applicability of the multi-regions division localization. Then, computer simulations are employed to evaluate the validity and the performance of multi-regions division localization and optimized multi-regions division localization. Finally, the efficiency of the proposed approach is confirmed via system experiments. The experimental results are consistent with simulation results which further indicate that the presented approach holds great potential in effective underwater vehicles localization for confined structured water scenarios.
Control System Design Automation Using Reinforcement Learning

Author(s): Mirzaei Buini, Hamid | Advisor(s): Givargis, Tony | Abstract: Conventional control theory has been used in many application domains with great success in the past decades. However, novel solutions are required to cope with the challenges arising from complex interaction of fast growing cyber and physical systems. Specifically, integration of classical control methods with Cyber-Physical System (CPS) design tools is a non-trivial task since those methods have been developed to be used by human experts and are not intended to be part of an automatic design tool. On the other hand, the control problems in emerging Cyber-Physical Systems, such as intelligent transportation and autonomous driving, cannot be addressed by conventional control methods due to the high level of uncertainty, complex dynamic model requirements and operational and safety constraints.In this dissertation, a holistic CPS design approach is proposed in which the control algorithm is incorporated as a building block in the design tool. The proposed approach facilitates the inclusion of physical variability into the design process and reduces the parameter space to be explored. This has been done by adding constraints imposed by the control algorithm.Furthermore, Reinforcement Learning (RL) as a replacement for convection control solutions are studied in the emerging domain of intelligent transportation systems. Specifically, dynamic tolling assignments and autonomous intersection management are tackled by the state-of-the-art RL methods, namely, Trust Region Policy Optimization and Finite-Difference Gradient Descent. Additionally, Q-learning is used to improve the performance of an embedded controller using a novel formulation in which cyber-system actions, such as changing control sampling time, is combined with the physical action set of the RL agent. Using the proposed approach, it is shown that the power consumption and computational overhead of the embedded control can be improved.Finally, to address the current lack of available physical benchmarks, an open physical environment benchmarking framework is introduced. In the proposed framework, various components of a physical environment are captured in a unified repository to enable researchers to define and share standard benchmarks that can be used to evaluate different reinforcement algorithms. They can also share the realized environments via the cloud to enable other groups perform experiments on the actual physical environments instead of currently available simulation-based environments.