Miracle-dz/UniFAR-FTUs huggingface dataset:
Composite Measures of Supercomputer Technology

We have developed composite measures of supercomputer technology, reflecting various factors of supercomputers using Martino’s scoring model. CPUs, accelerators, memory, interconnection networks, and power consumption are chosen as factors of the model. The weight values of the factors are derived based on a survey of 129 domestic and international experts. The measured values are then standardized to integrate measurement units of the factors in the model. This model has been applied to 50 supercomputers, and rank correlation analysis was performed using representative measures. As a consequence, the ranking drastically changes except for the 1st and 2nd supercomputers on the TOP500. In addition, the characteristics of memory and interconnection networks influence the ranking, and the results demonstrate that the proposed model has low correlations with HPL and HPCG but a high correlation with Green500. This indicates that power consumption is an important factor that has a significant effect on the measures of supercomputer technology. In addition, it is determined that the differences between the HPL ranking and the proposed model ranking are influenced by power consumption, CPU theoretical peak performance, and main memory bandwidth in order of significance. In conclusion, the composite measures proposed in this study are more suitable for comprehensively describing supercomputer technology than existing performance measures. The findings of this study are expected to support decision
Corotated meshless implicit dynamics for deformable bodies

This paper proposes a fast, stable and accurate meshless method to simulate geometrically non-linear elastic behaviors. To address the inherent limitations of finite element (FE) models, the discretization of the domain is simplified by removing the need to create polyhedral elements. The volumetric locking effect exhibited by incompressible materials in some linear FE models is also completely avoided. Our approach merely requires that the volume of the object be filled with a cloud of points. To minimize numerical errors, we construct a corotational formulation around the quadrature positions that is well suited for large displacements containing small deformations. The equations of motion are integrated in time following an implicit scheme. The convergence rate and accuracy are validated through both stretching and bending case studies. Finally, results are presented using a set of examples that show how we can easily build a realistic physical model of various deformable bodies with little effort spent on the discretization of the domain.
Application of artificial neural network for design: a case of paper helicopter

The design engineering is one of essential work in modern manufacturing environment. The optimization is principal technique to be used widely for searching the solution. However, primary process of optimization is to know the relation between design input parameters and target output. In this work, an artificial neural network (ANN) approach as an intelligent algorithm is proposed to construct the relation and also provides it in form of mathematic modeling. Even though the ANN modeling is so call a backblock due to difficulty to understand complicated equations, it is simply constructed by automate iteration process. A case of paper helicopter is used as an example of the application. The classical 2k Factorial design is used to provide an experiment plan to create training and testing data. 93 experiments are carried out. The architecture of ANN is set according to lowest Mean square error (MSE) of training and testing procedure. The result of 5-10-1 architecture has shown ability to accurately predict output, landing time, with MSE of 0.012. With such a highly quantitative accuracy of results, the developed model using the neural network approach can be used for finding the suitable input parameters to achieve a desired target output. In this case, the design of dimension (A) Depth of cut wing is 1.3 cm. , (B) Length of wing is 12.9 cm. , (C) Length of body is 9.0, (D) Width of body is 2.0 cm. , and (E) Depth of cut body is 0 cm. yield the lowest area of a paper helicopter that can meet the target landing time, 2.85 + 5% second.
Wireframe Parsing With Guidance of Distance Map

We propose an end-to-end method for simultaneously detecting local junctions and global wireframe in man-made environment. Our pipeline consists of an anchor-free junction detection module, a distance map learning module, and a line segment proposing and verification module. A set of line segments are proposed from the predicted junctions with guidance of the learned distance map, and further verified by the proposal verification module. Experimental results show that our method outperforms the previous state-of-the-art wireframe parser by a descent margin. In terms of line segments detection, our method shows competitive performance on standard benchmarks. The proposed networks are end-to-end trainable and efficient. <xref ref-type="fn" rid="fn1"><sup>a</sup></xref><fn id="fn1"><label><sup>a</sup></label><p>The code will be released on github for reproduction of the results. </p></fn>
EMG-based Force Estimation using Artificial Neural Networks

In this paper, the surface electromyogram (sEMG) signals acquired from linear surface electrode arrays, placed on the long head and short head of biceps brachii, and brachioradialis during isometric contractions are used to estimate force induced at the wrist using an artificial neural network (ANN). We extracted some features, in time and frequency domain, from sEMG signals and used them as inputs to the ANN model. Different hidden layer sizes were considered to investigate its effect on the model accuracy and find the appropriate number of neurons for our problem. Also, we studied the model accuracy, where we used features individually as the model’s input. The best accuracy, during train, validation and test, was obtained for the maximum number of sEMG features.
Design and Analysis of Efficient Parallel Bayesian Model Comparison Algorithms

A common task in science and engineering is evaluating how well a mathematical model describes a set of observations. Bayesian model comparison provides a rational and consistent method for applying logic and probability to the problem of evaluating models. Model comparison requires numerical techniques that are usually very time consuming to run. This dissertation proposes extensions to several existing numerical model comparison techniques, including nested sampling and thermodynamic integration, that incorporate parallel algorithm design to achieve significant speed-ups. Serial computer performance gains have slowed in recent years, and most processing speed improvements are seen in the area of parallel architectures. This work discusses the design, theoretical analysis, and empirical analysis of these algorithms, focusing on the performance of these algorithms with respect to accuracy and run time. Many disciplines in science and engineering make use of existing model comparison techniques. This work aims to save investigators in these disciplines time, and potentially attract those who may have been put off by time complexity concerns, by developing a general approach to model comparison that takes full advantage of modern parallel computing platforms.
Improved Dynamic Modeling and Robust Control of Autonomous Underwater Vehicles

In this dissertation, we seek to improve the dynamic modeling and control of autonomous underwater vehicles (AUVs). We address nonlinear hydrodynamic modeling, simplifying modeling assumptions, and robust control for AUVs. In the literature, various hydrodynamic models exist with varying model complexity and with no universally accepted model. We compare various hydrodynamic models traditionally employed to predict the motion of AUVs by estimating model coefficients using least-squares and adaptive identifier techniques. Additionally, we derive several dynamic models for an AUV employing varying sets of simplifying assumptions. We experimentally assess the efficacy of invoking typical assumptions to simplify the equations of motion. For robust control design, we develop a procedure for designing robust attitude controllers based on loop-shaping ideas. We specifically address the challenge of adjusting the desired actuator bandwidth in a loop-shaping design framework. Finally, we present a novel receding horizon H∞ control algorithm to improve the control of autonomous vehicle systems working in high-disturbance environments, employing a Markov jump linear system framework to model the stochastic and non-stationary disturbances experienced by the vehicle. Our main results include a new Bounded Real Lemma for stability analysis and an output feedback H∞ control synthesis algorithm. This work uses numerical simulations and extensive field trials of autonomous underwater vehicles to identify and verify dynamic models and to validate control algorithms developed herein. Improved Dynamic Modeling and Robust Control of Autonomous Underwater Vehicles
VLSI Architecture for Enhanced Approximate Message Passing Algorithm

Compressed sensing (CS) enables the reconstruction of sparse signals from a small number of linear measurements. However, sparse signal recovery algorithms require significant computational effort, even for problems of moderate size, and make their hardware realization a highly challenging task. In this paper, we present a novel architecture for the reconstruction of the sparse signal using the recently proposed Enhanced Approximate Message Passing (EAMP) algorithm. The EAMP algorithm shows better performance in terms of convergence rate and sparsity measurement trade-off as compared to AMP, IST and IHT techniques. The execution time of the proposed design has been reduced by maximizing parallelism with an appropriate level of unfolding. It receives CS measurements (y) and the number of non-zero elements (K) in the input vector from the encoder. Depending on the value of K, one sensing matrix (<inline-formula> <tex-math notation="LaTeX">${\Phi }_{{{j}}}$ </tex-math></inline-formula>) is selected from the code book <inline-formula> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula>. The reconstruction algorithm (EAMP) recovers the input vector (<bold>s</bold>) by using y and <inline-formula> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula>. Bitonic sorting has been used to identify the thresholding value in the EAMP algorithm. The proposed architecture for EAMP algorithm has been synthesized using Synopsys design compiler with 65 nm technology CMOS standard cell libraries. It occupies 719.15 KGE and consumes 315.5 mW power at a frequency of 400 MHz. It is suitable for any arbitrary sparsity-based signal restoration and CS problems.
Fatigue Topology Optimization Based on Global Stress Constraint Method

In topology optimization design for continuous structure, common mechanical properties considered in optimization model focus on structural strength, stiffness and stability. However, fatigue characteristic of structure is easily ignored, which is one of the main causes of engineering failure. Therefore, it is necessary to consider the fatigue characteristic for the optimization design of the structure. In order to investigate the influence of fatigue on structural topology in optimization design, fatigue life filter function is introduced and then the model and solution method of fatigue topology optimization for continuous structures are proposed based on independent continuous mapping method (ICM). The fatigue topological optimization model is established with structure minimum weight as objective and fatigue life as constraint, and the model is solved by global stress constraint method. The results extend the ICM method and this basic theory provides a new idea for fatigue topology optimization.
Mapping perception of consumer antivirus software with multidimensional scaling method

Antivirus software industry is growing rapidly in the world in 2018. The domestic antivirus software industrymust be able to compete on a global scale. To face free trade, Indonesia's antivirus software industry must be able toknow its position in the minds of consumers, especially domestic consumers. In this research, Smadav will representthe antivirus software industry from Indonesia. In this research want to know how the position of smadav comparedwith its current competitors, namely Avast, Avira, AVG, Kaspersky, McAfee and Norton. This research is only done tomap antivirus software based on similarity according to respondent's perception. This research uses Multidimensionalscaling (MDS) method through SPSS software program version 23. The results showed that there are three groups ofdifferent antivirus software based on similarity level according to the respondent's perception. On the two-dimensionaland three-dimensional maps Norton antivirus software, Avast and Avira have similar resemblance according to therespondent's perception, because the location is closest and is in the same quadrant. Smadav differs according toperceptions of respondents. AVG, McAfee and Kaspersky have similarities according to respondents' perceptions.
Learning patterns from sequential and network data using probabilistic models

The focus of this thesis is on developing probabilistic models for data observed over temporal and graph domains, and the corresponding variational inference algorithms. In many real-world phenomena, sequential data points that are observed closer in time often exhibit higher degrees of dependency. Similarly, data points observed over a graph domain (e.g., user interests in a social network) may exhibit higher dependencies with lower degrees of separation over the graph. Furthermore, the connectivity structures that define the graph domain can also evolve temporally (i.e., temporal networks) and exhibit dependencies over time. The data sets observed over temporal and graph domains often (but not always) violate the independent and identically distributed (i.i.d.) assumption made by many mathematical models. The works presented in this dissertation address various challenges in modelling data sets that exhibit dependencies over temporal and graph domains. In Chapter 3, I present a stochastic variational inference algorithm that enables factorial hidden Markov models for sequential data to scale up to extremely long sequences. In Chapter 4, I propose a simple but powerful Gaussian process model that captures the dependencies of data points observed on a graph domain, and demonstrate its viability in graph-based semi-supervised learning problems. In Chapter 5, I present a dynamical model for graphs that captures the temporal evolution of the connectivity structures as well as the sparse connectivity structures often observed in temporal real network data sets. Finally, I summarise the contributions of the thesis and propose several directions for future works that can build on the proposed methods in Chapter 6.
Syntharch: Interactive Image Search with Attribute-Conditioned Synthesis

The use of interactive systems has been found to be a promising approach for content-based image retrieval, the task of retrieving a specific image from a database based on its content. These systems allow the user to refine the set of results iteratively until the target is reached. In order to proceed with the search efficiently, conventional methods rely on some shared knowledge between the user and the system, such as semantic visual attributes of the images. Those approaches demand the images to be semantically labeled and introduce a semantic gap between the two parties’ understanding. In this paper, we explore an alternative approach to interactive image search where feedback is elicited exclusively in visual forms, therefore eliminating the semantic gap and allowing for a generalized version of the method to operate on unlabeled databases. We present Syntharch, a novel interactive image search approach which uses synthesized images as options for feedback, instead of asking textual questions to gain information on the relative attribute values of the target image. We further demonstrate that by using synthesized images rather than real images retrieved from the database as feedback options, Syntharch causes less confusion to the user. Finally, we establish that our proposed search method performs similarly or better in comparison to the conventional approach.
Smart attendance system based on frequency distribution algorithm with passive RFID tags

Staff attendance information has always been an important part of corporate management. However, some opportunistic employees may consign others to punch their time cards, which hampers the authenticity of attendance and effectiveness of record keeping. Hence, it is necessary to develop an innovative anti-cheating system for office attendance. Radio-Frequency IDentification (RFID) offers new solutions to solve such problems because of its strong anti-interference capability and non-intrusiveness. In this paper, we present a smart attendance system that extracts distinguishable phase characteristics of individuals to enable recognition of various targets. A frequency distribution histogram is extracted as a fingerprint for recognition and the K-means clustering method is utilized for more fine-grained recognition of targets with similar features. Compared with traditional attendance mechanisms, RFID-based attendance systems are based on living biological characteristics, which greatly reduces the possibility of false records. To evaluate the performance of our system, we conducted extensive experiments. The results of which demonstrate the efficiency and accuracy of our system with an average accuracy of 92%. Moreover, the system evaluation shows that our design is robust against differences in the clothing worn and time of day, which further verifies the successful performance of our system.
Performance Analysis of VPN Remote Access Tunnels

The purpose of the study is to analyze the efficiency of communication with the serverusing the methods of secure remote access, as well as checking and comparing the quality of servicesprovided by the server depending on the method of secure remote connection. The article focuses onVPN technology implemented in the latest Windows Server 2012 R2 operating system.
An OTA-Less Second-Order VCO-Based CT $\Delta\Sigma$ Modulator Using an Inherent Passive Integrator and Capacitive Feedback

Voltage-controlled oscillator (VCO)-based continuous-time <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> modulators (CTDSMs) are deemed attractive for deep-scaled process. However, to realize high-order noise shaping, existing works still employ operational transconductance amplifiers (OTAs), which limits the benefits brought by the VCO. This article presents an OTA-less second-order VCO-based CTDSM featuring a hybrid passive-VCO architecture. It exploits the gain-efficient and scaling-friendly nature of the VCO integrator, enabling the use of a fully passive loop filter to increase the order with minimum power. The proposed architecture is realized in a low-cost, low-noise cascaded-integrator feedback structure that makes use of the parasitic effect of the VCO as an inherent passive stage. Fabricated in 40-nm CMOS, the prototype occupies 0.028 mm<sup>2</sup> of active area and consumes 590 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> when sampling at 330 MHz. The CTDSM achieved peak Walden figure of merits of 22 fJ/step with 68.7-dB signal to noise and distortion ratio (SNDR) over 6-MHz bandwidth.
Marc: Multi-Granular Representation Learning for Networks Based on the 3-Clique

Network embedding is of paramount importance in many real applications, such as node classification, network visualization, and link prediction. Existing methods can effectively encode different structural properties into representations. Most of them are single-granular representation learning methods that do not enable the network to be easily analyzed at various scales. There are many kinds of hierarchical structures in reality. In this paper, we propose a novel algorithm multi-granular representation learning for networks based on the 3-clique named Marc. It makes the representations of the current network be formed by both considering the structure of this granular network and inheriting the coarser network representation results. Firstly, we propose the 3-clique coarsening strategy. A 3-clique is coarsened to be a supernode in the coarser network. These coarsened networks with different granularities, preserve the original network’s main structure. Secondly, we use the single-granular optimization objective function to obtain the nodes’ representations of each granular network. Finally, the refinement model learns the nodes’ representations, starting from the coarsest network. The learned representations of supernodes serve as good initializations for embedding the corresponding coarsened nodes from the finer network. This process is repeated until we obtain an embedding for each node in the original network which preserves the relationship among multi-granular networks. The experimental results on five public datasets, Wiki, BlogCatalog, Cora, CiteSeer, and DBLP, demonstrate that Marc has a better Macro F1 value for classification tasks than the baseline methods.
A review on medical image segmentation: techniques and its efficiency

Image segmentation is the procedure of separating an image into significant areas based on similarity or heterogeneity measures and it is widely used in many fields that involve digital imaging including the medical field. Medical images from Computed Tomography, Magnetic Resonance Imaging and Mammogram require a proper segmentation technique to decompose the images into parts for further analysis. However, a standard methodology for any type of medical image segmentation is yet to be developed. The current image segmentation techniques and its efficiency will be evaluated in order to discover the technique that is most appropriate to be used for medical image segmentation. Researches carried out on image segmentation techniques between the periods of 2000 to 2016 are analysed and examined. This study specifically compares the techniques by analysing the performance of each algorithm on breast cancer modalities.
ENERGY-AWARE SECURITY ROUTING PROTOCOL FOR WSN IN BIG-DATA APPLICATIONS

The dense deployment of the wireless sensor networks has caused enormous data flow leading to a Big-Data generation. To enable a continuous transmission for the huge volume of data packets, it becomes necessary to readapt the routing protocol to facilitate the routing in handling the Big-data scenario. Since energy is the main constraint in the wireless sensor network, as the sensor are battery powered, and the routing methods consuming enormous of the energy for route discovery thereby reducing the network life time, many conventional methods were developed to address the problem of energy consumption with the, increased network latency, overhead, security issues and delay. So the paper proposes the energy-aware routing protocol that could handle the security issues, arising in the enormous data generation satisfying the QOS requirements. The node with substantial resources are selected to overcome the problems of energy consumption and the network life time handling the security issues , delay, network latency and the overhead problem. Further validation of the proposed method in the Network Simulator-3 is performed to evaluate the network latency, packet delivery ratio, throughput, power consumption, network lifetime and Cost.