Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy‐aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU‐GPU architectures

By means of the availability of mechanisms such as Dynamic Voltage and Frequency Scaling (DVFS) and heterogeneous architectures including processors with different power consumption profiles, it is possible to devise scheduling algorithms that are aware of both runtime and energy consumption in parallel programs. In this paper, we propose and evaluate a multi‐objective (more specifically, a bi‐objective) approach to distribute the workload among the processing cores in a given heterogeneous parallel CPU‐GPU architecture. The aim of this distribution may be either to save energy without increasing the running time or to reach a trade‐off among time and energy consumption. The parallel programs considered here are master‐worker evolutionary algorithms where the evaluation of the fitness function for the individuals in the population demands the most part of the computing time. As many useful bioinformatics and data mining applications exhibit this kind of parallel profile, the proposed energy‐aware approach for workload scheduling could be frequently applied.
Autonomous Perpendicular And Parallel Parking Using Multi-Sensor Based Control*

This paper addresses the perpendicular and parallel parking problems of car-like vehicles for both forward and reverse maneuvers in one trial by improving the work presented in [1] using a multi sensor based controller with a weighted control scheme. The perception problem is discussed briefly considering a Velodyne VLP-16 and a SICK LMS151 as the sensors providing the required exteroceptive information. The results obtained from simulations and real experimentation for different parking scenarios show the validity and potential of the proposed approach.
The multi-source spatial data management system based on geological information metadata standard

Based on analyzing the characteristics of spatial data in combination with current spatial data storage technology,this study implements the organization and management of multi-source spatial data and builds the Multi-source Spatial Data Management System.For implementing the system,this study solves the problems of spatial data thematic diversity and data exchange by extending the metadata standards,solves the issues like large amounts of data,distributed source and heterogeneous data format using single machine multiple SDE data storage technology,and finally,in order to solve the problem of fast retrieval localization of spatial data,systematically builds the spatial index database based on single machine multiple SDE.The system lays a good foundation for solving thematic diversity,data sharing and exchange which will be met in the information platform construction of multi-source spatial data for the applications of resource and environmental remote sensing data.
Chinese Word Segmentation and Unknown Word Extraction by Mining Maximized Substring

Chinese word segmentation is an initial and important step in Chinese language processing. Recent advances in machine learning techniques have boosted the performance of Chinese word segmentation systems, yet the identiﬁcation of out-of-vocabulary words is still a major problem in this ﬁeld of study. Recent research has attempted to address this problem by exploiting characteristics of frequent substrings in unlabeled data. We propose a simple yet eﬀective approach for extracting a speciﬁc type of frequent substrings, called maximized substrings, which provide good estimations of unknown word boundaries. In the task of Chinese word segmentation, we use these substrings which are extracted from large scale unlabeled data to improve the segmentation accuracy. The eﬀectiveness of this approach is demonstrated through experiments using various data sets from diﬀerent domains. In the task of unknown word extraction, we apply post-processing techniques that eﬀectively reduce the noise in the extracted substrings. We demonstrate the eﬀectiveness and eﬃciency of our approach by comparing the results with a widely applied Chinese word recognition method in a previous study.
A Sense of Communal Belonging in Digital Space: The Case of the 3.11 Disaster

This article examines the role of social media in disaster communication and its potential to foster a sense of communal belonging through shared emotional connection, using the case study of the 3.11 disaster in Japan. The 2011 triple disaster had profound implications for new interactions among individuals, as the earthquake not only brought destruction to the surrounding environment, but also reconfigured many people’s sense of place, security and communal belonging. I argue that, in a post-disaster context, social media had substantial implications for individuals’ perceptions of belonging to local and regional communities, as it introduced new dimensions and forms of interaction among individuals, regardless of temporal and spatial barriers. Qualitatively approached, this study examines the evolving potential of social media to serve as a space where individuals can experience and express closeness in time of crisis, creating and maintaining new forms of affective communities in digital space. Based on in-depth interviews and social media data from Japan that relates to the 3.11 disaster, this article sheds light on how social media can enable users to experience a sense of belonging to local, regional and global communities through multi-level interaction with similar others.
Hybridized Support Vector Machine and Recursive Feature Elimination with Information Complexity

In statistical data mining research, datasets often have nonlinearity and at the same time high-dimensionality. It has become difficult to analyze such datasets in a comprehensive manner using traditional statistical methodologies. In this paper, a novel wrapper method called SVM-ICOMP-RFE based on a hybridized support vector machine (SVM) and recursive feature elimination (RFE) with information-theoretic measure of complexity (ICOMP) is introduced and developed to classify high-dimensional data sets and to carry out subset selection of the features in the original data space for finding the best subset of features which are discriminating between the groups. Recursive feature elimination (RFE) ranks features based on information complexity (ICOMP) criterion. ICOMP plays an important role not only in choosing an optimal kernel function from a portfolio of many other kernel functions, but also in selecting important subset(s) of features. The potential and the flexibility of our approach are illustrated on two real benchmark data sets, one is ionosphere data which includes radar returns from the ionosphere, and another is aorta data which is used for the early detection of atheroma most commonly resulting heart attack. Also, the proposed method is compared with other RFE based methods using different measures (i.e., weight and gradient) for feature rankings.
A pattern language: designing a hazard information map interface for community-based users

This paper explains guidelines that arose from a collaborative research project that aimed to make map-based bushfire information more accessible to people in remote and regional Australian communities. The MyFireWatch web application was a practical outcome from this research and was the result of several iterations of user and service-provider engagement. This application delivers a web-based interface that works on desktop and mobile devices which displays bushfire locations around Australia in near real-time. As a way of generalising from the work undertaken that resulted in the MyFireWatch application, guidelines were created to inform others working in similar domains. These guidelines are presented here in the form of a pattern language and are intended to inform the design of similar systems. Pattern language has previously been used in architecture, software engineering and interaction design as a means of exchanging knowledge in a way that provides specific solutions to recurring problems, yet these solutions are generalised so that they can be applied in different scenarios. The pattern language described here is intended to encourage others who may be working with map-based hazard information to consider various aspects of the interface and its functionality. It is hoped that in doing so, communities in Australia and beyond will find such systems more accessible, intuitive and easy to use.
Automatic Contrast Enhancement by Transfer Function Modification

In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous histogram‐based global contrast enhancement techniques employ histogram modification, whereas we propose a direct TFM technique that considers the mean brightness of an image during contrast enhancement. The mean point shifting method using a transfer function is proposed to preserve the mean brightness of an image. In addition, the linearization of transfer function technique, which has a histogram flattening effect, is designed to reduce visual artifacts. An attenuation factor is automatically determined using the maximum value of the probability density function in an image to control its rate of contrast. A new quantitative measurement method called sparsity of a histogram is proposed to obtain a better objective comparison relative to previous global contrast enhancement methods. According to our experimental results, we demonstrated the performance of our proposed method based on generalized measures and the newly proposed measurement.
Impact of Failure Prediction on Availability: Modeling and Comparative Analysis of Predictive and Reactive Methods

Predicting failures and acting proactively have a potential to improve availability as a correct prediction and a successful mitigation may bring a reward resulting in decrease of downtime and availability improvement. But, conversely, each incorrect prediction may introduce additional downtime (penalty). Therefore, depending on the quality of prediction and the system parameters, predictive fault-tolerance methods may improve or may degrade availability in comparison to the reactive ones. We first derive taxonomies of fault-tolerant techniques and policies to differentiate between reactive and proactive policies that are further classified as systematic and predictive. To evaluate whether a predictive policy improves availability or not, we derive an analytical model for availability quantification. We use Markov chains to extend steady-state availability equation to include: precision and recall, penalty and reward, mitigation success probability and potential failure rate increase due to the prediction load. We also derive an A-measure to optimize failure prediction for increasing availability. In our conclusion, precision and recall have comparable impact on availability as changing MTTF and MTTR. To validate the model we also simulate and analyze availability of a virtualized server with exponential distribution of failure and repair rates.
Exploring the backward swimming ability of a robotic fish

This article investigates the capability for backward swimming of a carangiform-fish-like robot with only three joints. A simple dynamic model based on a fixed point, a point in the body without perpendicular oscillation, is first developed to analyze the feasibility of backward motion for the robot. Through this theoretical analysis, we find that the fixed point lies closer to the robotic fish tail with higher backward swimming speeds. Combining the theoretical analysis with experimental optimization, we further explore backward swimming patterns using particle swarm optimization. After a series of online optimal experiments, we find several locomotion gaits that can make the robotic fish swim backward, and the corresponding fixed points are similarly located near the tail. The backward swimming velocity is strongly correlated with the fixed point position along the robotic fish body, which verifies the effectiveness of our fixed-point model.
Infrasound threat classification: a statistical comparison of deep learning architectures

Infrasound propagation through various atmospheric conditions and interaction with environmental factors in- duce highly non-linear and non-stationary effects that make it difficult to extract reliable attributes for classi- fication. We present featureless classification results on the Library of Typical Infrasonic Signals using several deep learning techniques, including long short-term memory, self-normalizing, and fully convolutional neural net- works with statistical analysis to establish significantly superior models. In general, the deep classifiers achieve near-perfect classification accuracies on the four classes of infrasonic events including mountain associated waves, microbaroms, auroral infrasonic waves, and volcanic eruptions. Our results provide evidence that deep neural network architectures be considered the leading candidate for classifying infrasound waveforms which can directly benefit applications that seek to identify infrasonic events such as severe weather forecasting, natural disaster early warning systems, and nuclear weapons monitoring.
Fine-Grained Virtual Network Embedding in 5G Hierarchical Network Infrastructure

Resource allocation and deployment of network Service Function Chain (SFC) is a vital problem of 5G network virtualization. By introducing SDN and NFV technology, the core network is reclassified as the control layer, forwarding layer and scheduling layer. A Virtual Network Embedding algorithm based on subgraph isomorphism (VNE-MSI) is proposed to allocate resource of each layer in isolated way. The proposed heuristic algorithm collaboratively maps decoupling Service Function Chain with backtracking strategy. Collaborative search strategy is designed according to the characters of virtual Evolved Packet Core. The experimental evaluations show that this method especially performs well in acceptance ratio and revenue-to-cost ratio. Keywords-network virtualization; 5G; virtual network embedding
Reliable JPEG Image Transmission Using Unequal Error Protection with Modified Non-Binary Turbo Codes

— JPEG is a widely deployed image compression standard used in several applications. However, JPEG image transmission is challenging and sophisticated strategies are required for reliable transmission. This paper proposes a JPEG image transmission using non-binary Turbo codes with Unequal Error Protection (UEP), extrinsic information scaling and iterative detection. UEP is achieved by applying a lower code-rate to protect the DC-layer of the image more efficiently and a higher code-rate for protecting the AC-layer. Additionally, the non-binary Turbo code is modified by scaling its extrinsic information to improve performance and by using a stopping criterion to limit the number of iterations required for decoding. The proposed UEP scheme provides a gain of at least 10 dB in Peak Signal to Noise Ratio (PSNR) over an Equal Error Protection (EEP) scheme when duo-binary Turbo codes are used and a gain of at least 14 dB in PSNR when triple binary codes are used, over a range of Eb/No values. Moreover, the use of triple binary turbo codes provides greater bandwidth efficiency.
Automated Power Optimization of Sequential Integrated Circuits through Approximate Computing

Approximate Computing is a paradigm promising to increase the power efficiency of integrated circuits by tolerating imprecisions in the circuit. It proposes e.g. to remove parts of a circuit or to over-scale the supply voltage to save power. In this work an approach is presented to efficiently perform the approximation of integrated circuits. Probabilistic fault injections are introduced to model a variety of approximation techniques and FPGA-based emulation is employed to accelerate the circuit analysis. In order to apply voltage over-scaling an analytical analysis approach has been developed.
Automated brain extraction and immersive exploration of its layers in virtual reality for the rhesus macaque MRI data sets

As we know, the rhesus macaque as a nonhuman primate is quite similar to a human being in genetics. Moreover, it has become essential in animal model anatomy and physiology in many modern medicine research such as the cardiovascular and cerebrovascular diseases in the recent years. This paper describes a pipeline from the raw rhesus macaque brain magnetic resonance imaging data to intuitive 3D inspection of its layers. Brain extraction is an initial step for subsequent analyses, but most of the existing methods so far are designed for human brain, which does not work well with the rhesus macaque. Firstly, we propose a reliable and efficient method to extract the brain from magnetic resonance imaging data sets based on dividing the brain into blocks. Then, we design a trapezoid opacity transfer function based on Compute Unified Device Architecture (CUDA)‐based real‐time volume ray casting, which is dedicated to volume rendering to make the inspection more intuitive. Besides, for an immersive exploration in virtual reality benefits understanding the volumetric data sets, so we also design a layer filter for the segmented rhesus macaque brain data sets, which facilitates the inspection of the interior in Region of Interest (ROI) by an intuitive bimanual interaction via a Leap Motion sensor. Our experiments prove usability and efficiency.
Overview of OLAP cubes, importance, build Considerations and querying with MDX.

An OLAP cube is a method of storing data in a multidimensional form, generally for reporting purposes. The purpose of an OLAP cube is to store data in such a way that an end user can slice and dice through the data and the storage structure itself (multidimensional) while allowing for very fast access to the data. Cubes are characterized by dimensions and measures. Dimensions refer to how the data is organized and are usually made up of character fields which tell us about the data. Measures, on the other hand, are those elements in the data which quantify something (a number) and can usually be additive . The business intelligence industry uses the word "cube" because it best describes the resulting data. The query language used to interact and perform tasks with OLAP cubes is multidimensional expressions (MDX). In this paper an attempt is made to give the overview, importance and build considerations of Olap cubes. Also querying of OLAP cube with MDX is discussed.
Performance Analysis of Islamic and Traditional Banks of Pakistan

This paper investigates the consequence of some CAMEL ratios, bank size, type of bank and governance structure on the financial performance of Banks. It also performs a relative analysis of Islamic and traditional banks of Pakistan. The comparative performance analysis is based on descriptive statistics and regression analysis. Fifteen traditional and two pure Islamic banks are selected for the analysis. The study period is from 2010-2017. Operational efficiency, asset quality, liquidity, capital adequacy, size, and profitability ratios along with governance structure are applied to identify the operational and financial performance of Islamic and traditional banks of Pakistan. The paper provides strong evidence that all variables such as CAMEL ratios, bank type and bank size except governance structure are highly significant in assessing bank performance. The findings reveal significant implications for policymakers in assessing Islamic and traditional bank performance in Pakistan, and ascertaining the direction of a future banking system in Pakistan. Findings of the study also underpin the awareness and confidence in Islamic banks of Pakistan. Furthermore, to the best of our knowledge, no comprehensive research in Pakistan has examined the performance of Islamic and traditional banks with variables under study on the current data set.
Evaluation method for lightning damage of carbon fiber reinforced polymers subjected to multiple lightning strikes with different combinations of current components

The aircraft lightning environment consists of four lightning current components with different parameters, which are known as lightning components A, B, C and D. The lightning damage of aeronautic carbon fiber reinforced polymer laminates subjected to multiple continuous sequential lightning current components with different timing combinations was experimentally evaluated. The experimental results indicated that the carbon fiber reinforced polymer laminates suffered serious lightning damage, including carbon fiber fracture, resin pyrolysis and delamination. Through an analysis of the lightning damage properties of carbon fiber reinforced polymers, the influential factors and evaluation methods of the lightning damage in carbon fiber reinforced polymer laminates were studied. Because the lightning damage evaluation method under a single lightning impulse was found to be inapplicable for the multiple continuous lightning strikes, a multi-factor evaluation method was proposed. In the multiple continuous lightning strike test, the damage depth was found to be closely related to lightning components A, B and D and could be estimated based on the amplitudes and rise rates of the applied lightning components. Increases in the damaged area after a lightning strike were driven by lightning component C due to its substantial thermal effects. The damaged area was evaluated on the basis of the parameters of the electrical action integral and the transfer charge. The research on the evaluation methods for carbon fiber reinforced polymer laminate lightning damage presented herein may provide experimental support and a theoretical basis for studying the lightning effect mechanism and optimizing material formulations, manufacturing processes and structural designs to achieve performance improvements for carbon fiber reinforced polymer laminates in the future.
Web Analytical Engine in the Big Data Era

Web search engines can only return a list of Web documents (the so-called ten blue links), whereas users may need high-order knowledge that is contained within the Web documents. The demand of analytical services atop the Web is becoming stronger with the rapid development of the internet and the increase of big Web data. The concept of “Web Analytical Engine”, which aims to provide analytical service atop the huge amount of Web documents, was introduced. A simple infrastructure was described and the key research problems for building such an engine were discussed.
Feedback-based IKP Solution with SMC for Robotic Manipulators: the SCARA Example

This paper presents a novel scheme for solving inverse kinematics problem (IKP) of a multi-link robotic manipulator. Important features of the proposed strategy are generality and simplicity regardless of the number of degrees of freedom (DOF) and geometry of the robot. The proposed method is a feedback strategy where the IKP solution is expressed as a dynamic control system whose goal is to maintain satisfactory trajectory tracking. As a simulation test to reveal the performance of proposed scheme, a four DOF Selective Compliance Assembly Robot Arm (SCARA) system is considered. Feedback law in proposed closed-loop solution method is selected as a combination of Sliding Mode Control (SMC) and Proportional-Derivative (PD) control for providing simplicity and robustness. Simulation results are used to show the efficacy of proposed IKP solution approach in comparison with commonly used neural networks (NN) based IKP solution method. Results reveal that proposed method yields the solution of IKP with satisfactory performance.