Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Thread Mapping for Maximizing Performance in Power-Efficient Multi-core Systems

Efficiently mapping threads to system cores is critical for achieving high performance and power efficiency in multicore systems. Conventional scheduling techniques do not take system heterogeneity and varying DVFS states into account and therefore do not produce efficient mappings. Additionally, since thread behavior varies throughout its execution, a fixed thread-to-core mapping is suboptimal. Dynamically scheduling threads to appropriate core states can significantly improve system throughput and decrease energy consumption. Scheduling decisions are of utmost importance in systems that aim to maximize performance. The problem of thread assignment in order to meet performance or power constraints has been shown to be NP-complete and therefore exceedingly expensive to solve online. This paper proposes a heuristic for dynamically assigning threads to system cores such that performance is maximized while minimizing the energy consumed. The heuristic can be applied to heterogeneous systems and homogeneous systems with DVFS capabilities. Simulation results show that the heuristic can be used online in hundred-core systems, with runtime overhead of less than 1 millisecond in worst cases for 256-core systems.
Is My Interface Beautiful?—A Computational Model-Based Approach

In this paper, we proposed a computational model to measure the beauty or aesthetics of an interface by analyzing the components associated with it. The three primary elements—text, image, and white space were considered for our study. Using support vector regression, we developed two different computational models to predict the aesthetics of the image and the text components present in an interface. To create the image aesthetics model, we considered the 11 features associated with image aesthetics. The 83 users rated a total of 250 images considered for our study. The average users’ rating and the 11 feature values were used to train and test our image aesthetics model. The experimental results showed that our model could predict image aesthetics an RMSE of 0.68. Another computational model of text aesthetics was developed by considering the six features of text elements. We designed 95 text samples by varying these features systematically. The 185 users rated the text samples. The six feature values along with the average users’ ratings were used to train and test our text aesthetics model. The model can measure text aesthetics with an RMSE of 0.58. We performed another empirical study to develop the white space aesthetics model. It was observed that the median scale is best suited for this. Finally, we created the interface esthetic model by integrating the three models—text, image, and white space. The 150 interfaces were designed to validate our final model. The experimental results showed that our proposed model could measure interface aesthetics with an RMSE of only 0.79.
A Novel Artificial Neural Network Scheme for Modelling of Nonlinear Soil Stress-Strain Modulus Exponent

The principal aim of this study was to develop and verify a new Artificial Intelligence model to predict the hyperbolic soil stress-strain parameter, namely the modulus exponent (n). To achieve the planned aim, artificial neural network was developed and trained, additionally, it targeted to provide an appropriate empirical model to predict the parameter n with high efficiency. A database of laboratory measurements encompasses total of (83) case records for modulus exponent (n). Four input parameters namely: Dry unit weight, Plasticity index, Confining stress, and Water content, are considered to have the most substantial influence on the nonlinear soil stress-train relationship parameter, which are used as individual input parameters to the developed the proposed model. Multi-layer perceptron class trained using back propagation approach in this work. The effect of several issues in relation to the proposed model construction such as artificial neural network geometry and internal parameters on the performance of the model is investigated. Information on the relative importance of the factors affecting the (n), is presented, and practical equations for its prediction are proposed.
SVD for Very Large Matrices: An Approach with Polar Decomposition and Polynomial Approximation

We propose a fast approximation method for singular value decomposition (SVD) based on polar decomposition (PD), which utilizes Chebyshev polynomial approximation (CPA). SVD is widely used in various applications in data mining, machine learning, and signal/image processing. However, very large computation times are usually required when large matrices need to be decomposed. SVD based on PD (SVD_PD) is computationally efficient compared to the traditional SVD approach, as the eigenvalue decomposition in its algorithm can be easily parallelized. However, the PD part of the algorithm still needs to be accelerated due to the requirement of QR decomposition. This computational burden poses a problem both for the traditional SVD and SVD_PD. In this paper, we propose an approximation method for PD using CPA. It can bypass QR decomposition, thus leading to the acceleration of SVD_PD. First, we show that PD can be performed by "filtering" the eigenvalues of DTD, where D is the target matrix to be decomposed. Then, the actual algorithm and approximate error bound are shown. The proposed approximate SVD can be implemented only by multiplying the matrices; that is, QR decomposition is no longer required. As a result, the approximation of PD with the proposed method significantly reduces the computation cost and can be easily parallelized. Experimental results show that the proposed CPA-based SVD_PD can decompose a matrix faster than the conventional approach, and the application of the proposed method does not affect performance.
Multiobjective Optimization Approach for a Portable Development of Reconfigurable Real-Time Systems: From Specification to Implementation

This paper deals with the reconfigurable real-time systems that should be adapted to their environment under real-time constraints. The reconfiguration allows moving from one implementation to another by adding/removing/modifying parameters of real-time software tasks which should meet related deadlines. Implementing those systems as threads generates a complex system code due to the large number of threads, which may lead to a reconfiguration time overhead as well as the energy consumption and the memory allocation increase. Thus this paper proposes a multiobjective optimization approach for reconfigurable systems called MO2R2S for the development of a reconfigurable real-time system. Given a specification, the proposed approach aims to produce an optimal design while ensuring the system feasibility. We focus on three optimization criteria: 1) response time; 2) memory allocation; and 3) energy consumption. To address the portability issue, the optimal design is then transformed to an abstract code that may in turn be transformed to a concrete code which is specific to a procedural programming (i.e., POSIX) or an object-oriented language (i.e., RT-Java). The MO2R2S approach allows reducing the number of threads by minimizing the redundancy between the implementation sets. By an experimental study, such optimization permits to decrease the memory allocation by 28.89%, the energy consumption by 40.2%, and the response time by 61.32%.
Joint Word- and Character-level Embedding CNN-RNN Models for Punctuation Restoration

The sequence-to-sequence modelling paradigm has been successfully used in automatic punctuation of text generated by Automatic Speech Recognizers (ASR), using bidirectional Recurrent Neural Networks (RNN), which map the word and/or acoustic event sequence to the punctuation sequence. The current paper proposes to enhance the word sequence-based system with a character-level model using a Convolutional Neural Network (CNN). CNNs are known to be useful as data-driven front-ends in machine perception, hence we believe that the proposed approach is relevant for cognitive infocommunications. We also evaluate a hybrid solution when the punctuation marks are jointly determined based on character- and on word-level features, and demonstrate significant improvement in punctuation performance. The performance evaluation is executed on a Hungarian Broadcast Dataset and on the IWSLT English dataset.
Inertial Yaw-Independent Velocity and Attitude Estimation for High-Speed Quadrotor Flight

This letter addresses the velocity and attitude estimation of a quadrotor using motor speeds and inertial measurement unit (IMU) data. This result is obtained through a novel filter that employs a first-order drag model, which allows the velocity to be observed through the drag forces acting on the quadrotor. These forces are measured with the IMU. Compared to our previous contribution [J. Svacha, K. Mohta, M. Watterson, G. Loianno, and V. Kumar, “Inertial velocity and attitude estimation for quadrotors,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. , Oct. 2018, pp. 1–9. ], decoupling the attitude into a yaw-tilt convention results in the tilt and velocity in the body-fixed frame being independent of the yaw. Thus, the velocity and attitude estimates are no longer affected by yaw drifts obtained from dead-reckoning procedures. The camera available on the vehicle can optionally be employed to recover the yaw in a computationally efficient way without requiring RANSAC, instead using the estimated velocity and attitude from the filter. The IMU is concurrently used to estimate the thrust, linear drag coefficients, and accelerometer biases. Experimental results on a quadrotor platform show the feasibility of the proposed approach.
Handbook of Applied Spatial AnalysisSoftware Tools, Methods and Applications

The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and textbooks that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors develop, from their particular perspective and insights, their own strategies for mapping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them. The chapters were rigorously refereed blindly by the contributors to this volume. Referee reports were sent to each author and changes made accordingly. We supervised this process to guarantee that authors received reviews that would be useful for finalizing their chapters. The soundness of the comments and ideas have contributed immensely to the quality of the Handbook. Fortunately, we were dealing with truly exemplary scholars, the most distinguished and sophisticated representatives of the fields of inquiry. We thank the contributors for their diligence, not only in providing extremely thoughtful and useful contributions, but also in meeting all deadlines in a timely manner and in following stringent editorial guidelines. Moreover, we acknowledge the generous support provided by the Institute for Economic Geography and GIScience, Vienna University of Economics and Business. Thomas Seyffertitz greatly assisted in keeping the project well organized. Last but not at least, we have benefitted greatly from the editorial assistance he and Ingrid Divis provided. Their expertise in handling several word processing systems, formatting, and indexing, together with their care and attention to detail, helped immeasurably.
Passenger-Centric Metrics for Air Transportation Leveraging Mobile Phone and Twitter Data

This paper aims at presenting a detailed analysis of domestic air passengers behavior during a major air-traffic disturbance, from two complementary passenger-centric perspective: a passenger mobility perspective and a passenger social media perspective. By leveraging over 5 billion records of mobile phone location data per day from a major carrier in the United States, passenger mobility can be reliably analyzed, no matter which airline the passengers fly on or which airport they fly to and from. Such information is currently unavailable to the major aviation stakeholders at such scale and can be used to establish performance benchmarks from a passenger's perspective. Combining it with a Twitter analysis provides a more detailed and passenger-focused analysis than the traditional flight-centric measurements used to evaluate the overall system performance. More generally, these two passenger-centric analysis could be implemented in real-time for a daily evaluation of the Air Transportation System, enabling a faster analysis of the impact of major disruptions, whether due to meteorological conditions or system failures.
Application of Instance-Based Entropy Fuzzy Support Vector Machine in Peer-To-Peer Lending Investment Decision

Loan status prediction is an effective tool for investment decisions in peer-to-peer (P2P) lending market. In P2P lending market, most borrowers fulfill the repayment plan; however, some of them fail to pay back their loans. Therefore, an imbalanced classification method can be utilized to discriminate such default borrowers. In this context, the aim of this paper is to propose an investment decision model in P2P lending market which consists of fully paid loans classified via the instance-based entropy fuzzy support vector machine (IEFSVM). IEFSVM is a modified version of the existing entropy fuzzy support vector machine (EFSVM) in terms of an instance-based scheme. IEFSVM can reflect the pattern of nearest neighbors entropy with respect to the change of its size instead of fixing it in unified neighborhood size. Therefore, IEFSVM allows the class change of nearest neighbors in the determination of fuzzy membership. Applying the model to the lending club dataset, we determine loans that are predicted to be fully paid. Then, we also provide a multiple regression model to generate an investment portfolio based on non-default loans that are predicted to yield high returns. Throughout the experiment, the empirical results reveal that IEFSVM outperforms not only EFSVM but also the six other state-of-the-art classifiers including the cost-sensitive adaptive boosting, cost-sensitive random forest, EasyEnsemble, random undersampling boosting, weighted extreme learning machine, and cost-sensitive extreme gradient boosting in terms of loan status classification. Also, the investment performance of the multiple regression model using IEFSVM is higher and more robust than that of two other benchmarks. In this regard, we conclude that the proposed investment model is a decent and practical approach to support decisions in the P2P lending market.
Automated classification of Bacterial Images extracted from Digital Microscope via Bag of Words Model

The performance recognition of bacteria cell images is an effective survey for treatment of various diseases caused by the bacteria. Many algorithms for bacteria classification are designed for the needs of analysis of large-scale microscopic image bacteria. However, the biologist interpretation is suffered from insufficient information and thus may lead to limited accuracy in the bacteria classification process. To handle this drawback, machine learning tools, and image analysis approaches tackled identification of different bacteria species for improving the clinical microbiology investigation. In the proposed study, 200 bacterial images for ten different bacteria species with 20 images for each specie are extracted from DIBaS (Digital Images of Bacteria Species dataset). This proposed framework is divided into image preprocessing phase which obtained by histogram equalization, feature extraction by Bag-of-words model and classification phase by Support Vector Machine (SVM). The main objective is to enhance the bacterial images and find the image feature descriptors from the enhanced images which allowing to classify the bacterial images. The experimental results provided an average accuracy of 97% with classifier speed for automated detection and classification of bacterial images which would greatly reduce the disease outbreaks in future researches.
A Method Of Content-based Image Retrieval For The Generation Of Image Mosaics

An image mosaic is an artistic work that uses a number of smaller images creatively combined together to form another larger image. Each building block image, or tessera, has its own distinctive and meaningful content, but when viewed from a distance the tesserae come together to form an aesthetically pleasing montage. This work presents the design and implementation of MosaiX, a computer software system that generates these image mosaics automatically. To control the image mosaic creation process, several parameters are used within the system. Each parameter affects the overall mosaic quality, as well as required processing time, in its own unique way. A detailed analysis is performed to evaluate each parameter individually. Additionally, this work proposes two novel ways by which to evaluate the quality of an image mosaic in a quantitative way. One method focuses on the perceptual color accuracy of the mosaic reproduction, while the other concentrates on edge replication. Both measures include preprocessing to take into account the unique visual features present in an image mosaic. Doing so minimizes quality penalization due the inherent properties of an image mosaic that make them visually appealing.
Pressurized intraperitoneal aerosol chemotherapy procedure for nonresectable peritoneal carcinomatosis: First Indian study

Abstract Background: Peritoneal carcinomatosis (PC) is a common evolution of abdominal cancers and is associated with poor prognosis. A few selected patients have option of cytoreductive surgery and hyperthermic intraperitoneal chemotherapy, but majority who are not eligible for curative approach can undergo pressurized intraperitoneal aerosol chemotherapy (PIPAC). It is an emerging field of research with major therapeutic potential. It is a safe and innovative approach, which enhances the effect of chemotherapy without major toxicity. Methods: Between June 2017 and December 2017, 21 PIPAC applications in seven patients with standard chemotherapy regimen every 6 weeks at 37°C and 12 mmHg for 30 min was performed. The patients' demographics, perioperative findings, adverse events, and outcomes were prospectively recorded. Results: Twenty-one PIPAC administrations were performed in 7 patients with PC from various pathologies. The median hospital stay was 1 day. All the patients had symptomatic relief with complete resolution of ascites. There was no major perioperative complications. CTCAE Grades 1 and 2 were observed in three patients, for abdominal pain and nausea. Renal and hepatic functions were not impaired. Of the seven patients, one patient had complete histological remission; three patients had partial response, one had stable disease and one patient had no response with clinical progression. Conclusion: Our results show the feasibility and safety of PIPAC in Indian patients. The procedure has low morbidity with no mortality with the short learning curve. It can be easily adapted for Indian patients with diffuse PC as a palliative option apart from systemic chemotherapy.
Domain Adaptation of End-to-end Speech Recognition in Low-Resource Settings

End-to-end automatic speech recognition (ASR) has simplified the traditional ASR system building pipeline by eliminating the need to have multiple components and also the requirement for expert linguistic knowledge for creating pronunciation dictionaries. Therefore, end-to-end ASR fits well when building systems for new domains. However, one major drawback of end-to-end ASR is that, it is necessary to have a larger amount of labeled speech in comparison to traditional methods. Therefore, in this paper, we explore domain adaptation approaches for end-to-end ASR in low-resource settings. We show that joint domain identification and speech recognition by inserting a symbol for domain at the beginning of the label sequence, factorized hidden layer adaptation and a domain-specific gating mechanism improve the performance for a low-resource target domain. Furthermore, we also show the robustness of proposed adaptation methods to an unseen domain, when only 3 hours of untranscribed data is available with improvements reporting upto 8.7% relative.
Exploring Cooperative Transboundary River Management Strategies for the Eastern Nile Basin

A water resource modeling process is demonstrated to support multistakeholder negotiations over transboundary management of the Nile River. This process addresses the challenge of identifying management options of new hydraulic infrastructure that potentially affects downstream coriparian nations and how the management of existing infrastructure can be adapted. The method includes an exploration of potential management decisions using a multiobjective evolutionary algorithm, intertwined with an iterative process of formulating cooperative strategies to overcome technical and political barriers faced in a transboundary negotiation. The case study is the addition of the Grand Ethiopian Renaissance Dam (GERD) and considers how its operation may be coordinated with adaptations to the operations of Egypt's High Aswan Dam. The results demonstrate that a lack of coordination is likely to be harmful to downstream riparians and suggest that adaptations to infrastructure in Sudan and Egypt can reduce risks to water supplies and energy generation. Although risks can be substantially reduced by agreed releases from the GERD and basic adaptations to the High Aswan Dam, these measures are still insufficient to assure that no additional risk is assumed by Egypt. The method then demonstrates how improvements to water security for both downstream riparians can be achieved through dynamic adaptation of the operation of the GERD during drought conditions. Finally, the paper demonstrates how the robustness of potential management arrangements can be evaluated considering potential effects of climate change, including increased interannual variability and highly uncertain changes such as increases in the future persistence of droughts.
Fourier-Mellin Transform and Fractal Coding for Secure and Robust Fingerprint Image Hashing

In this paper, we propose a novel scheme for generating a secure and robust hash using Fourier-mellin transform and fractal coding. First, Fourier-mellin transform is incorporated into the domain minutiae blocks due to its invariance property in order to improve performance under geometric operations, hence generating a fixed-length minutiae representation. Then the property of dimensionality reduction and texture compression is exploited using fractal coding in order to generate a robust and compact hash. To secure the system, encryption is performed on the extracted hash using a secret key. Experimental results demonstrated the robustness of our hashing scheme to a wide range of distortion manipulations. Additionally, performance of the proposed scheme is measured and compared with recent state-of-art techniques, and our scheme generally outperform the others.