Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Body Mass Index on Outcome Measures of the Patients with Penetrating Injuries; A Single Center Experience

Objective: To determine if there was any decrease in measures of injury severity or outcome with obese patients (body mass index greater than or equal to 30 kg/m2) as compared to non-obese patients (body mass index less than 30 kg/m2). Methods: We conducted a retrospective review of the trauma database maintained by Natividad Medical Center's Level 2-Trauma program. From July 1st, 2014 to July 1st, 2017 there were 371 cases of penetrating trauma in adults between the ages of 18-80 years old. Overall 311 patients had BMI data recorded. We divided these 311 patients into two groups: penetrating injury due to firearm (n= 198) and penetrating injury due to stabbing or piercing (n=113). We compared non-obese patients against obese patients for age, gender, Injury Severity Score (ISS), length of stay (LOS), Intensive Care Unit LOS, units of blood given, direct transfer from ED to operating room, and mortality. Results: A total of 311 patients were included in the study, 198 (63.6%) patients suffered from gunshot wounds and 113 (36.4) from stab or piercing wounds. The mean age was 33.6 ± 12.8 and there were 283 (91%) men among the victims. Overall 87 (28%) required emergent surgery and a 19 (6.1%) mortality rate was recorded. In the gunshot wound group there was no significant difference between non-obese and obese patients for age (p=0.400), gender (p=0.900), ISS (p=0.544), LOS (p=0.273), Intensive Care Unit LOS (p=0.729), units of blood given (p=0.300), or mortality (p=0.855). We found that in the stab or piercing group there was no significant difference between non-obese and obese patients for age (p=0.900), gender (p=0.900), ISS (p=0.580), LOS (p=0.839), Intensive Care Unit LOS (p=0.305), units of blood given (p=0.431), or mortality (p=0.321). Conclusion: Our findings indicate that in our patient population, there was no significant difference in markers of injury severity, morbidity, or mortality in adult non-obese patients as comparted with obese patients. Furthermore, there was no significant difference between the two groups in operative rates, suggesting that obesity may not confer a protective effect in penetrating trauma.
Unmixing of Mineralogical Clay Intimate Mixtures with Laboratory Hyperspectral Images

Mineralogical clays are intimately mixed in soils. Until now, there is not a clear view on which unmixing methods are the most effective in case of intimate mixtures with both clays and with other mineral. A laboratory approach is proposed in order to compare existing linear and nonlinear unmixing approaches in terms of performances. Laboratory images of pure clays of Montmorillonite, Illite and Kaolinite were preprocessed using 6 transformations commonly used in literature (SNV, CR, CWT, Hapke, 1st SGD, Log(l/R)). Prediction of abundances has been performed on non-preprocessed and preprocessed data with 2 linear unmixing methods (FCLS and MESMA) and 2 nonlinear unmixing methods (GBM and MLM). Results show a decrease of unmixing performance due to sample variability, which can be reduced using the appropriate preprocessing. However, no particular advantage has been found on using nonlinear over linear unmixing approaches for clay minerals.
Understanding Streaming Music Diffusion in a Semi-Closed Social Environment

Music social networks play a role in the diffusion of music. There are different ways a piece of music reaches people in a network: through the influence of social connections or via the discovery of external information, such as mass media, newspapers, etc. This empirical study uses over 10 months of user listening data from a music social network to examine the effects of external information on streaming music diffusion at the macroand micro-levels. The data include weekly listening records for 557,554 users. Our results suggest that external information is a significant driver of increased streaming music diffusion, in comparison to innetwork influences. We also found evidence of variation in the different influences, such as for a scale effect, the validity and type of information shared, and the impact of geolocation. These insights can be used to promote music and design personalized music recommendations.
Multi-Scale Structure Extraction for Hyperspectral Image Classification

In this paper, a novel multi-scale structure extraction based spectral-spatial hyperspectral image classification method is proposed, which consists of the following steps. First, the spectral dimension of the hyperspectral image is reduced by averaging adjacent spectral bands. Then, in order to extract the multi-scale significant structural features (MSFs) which are insensitive to image noise and texture, a relative total variation based structure extraction method is applied on the dimension reduced hyperspectral image. Finally, the MSFs are fused together with the kernel principal component analysis (KPCA), so as to obtain the kernel PCA fused multi-scale structural features (KPCA-MSFs) for classification. Experiments conducted on a real hyperspectral image demonstrate the outstanding performance of the proposed approach over several state-of-the-art spectral-spatial classifiers, especially when the image is corrupted by serious scene noise.
AIS Electronic

With the rise of big data as a strategic tool in contemporary firms, researchers and practitioners have been exploring the ways in which such investments yield the maximum business value. Building on the growing importance of information governance as a means of attaining business value form big data investments, this study examines how it influences a firm’s dynamic capabilities, and how environmental factors impact these effects. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling (PLS-SEM), results show that big data governance has a positive and highly significant effect on sensing, seizing, and transforming capabilities. These effects are magnified under varying combinations of environmental conditions.
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models

Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and provide very accurate estimates for nonlinear problems, especially in parameter retrieval. An addition to a predictive mean function, GPs come equipped with a useful property: the predictive variance function which provides confidence intervals for the predictions. The GP formulation usually assumes that there is no input noise in the training and testing points, only in the observations. However, this is often not the case in Earth observation problems where an accurate assessment of the instrument error is usually available. In this paper, we showcase how the derivative of a GP model can be used to provide an analytical error propagation formulation and we analyze the predictive variance and the propagated error terms in a temperature prediction problem from infrared sounding data.
Cnn Based Renormalization Method for Ship Detection in Vhr Remote Sensing Images

Ship detection with very high resolution (VHR) remote sensing image has recently been an attractive topic due to rapid development of deep learning. Current researches on ship detection are generally confronted with a big challenge that existing methods failed to get high quality of object proposal with good intersection-over-union (IOU) before detection. In this paper, a Convolutional Neural Network (CNN) based renormalization method is proposed to improve the quality of object proposal. First, CNN is used to predict shape information of candidate ships' which are involved with rotation, location and scale in patches. Then, a renormalization net is designed to adjust the candidate ships in patches by correcting the shape information and renormalizing it to uniform patch. In this way, good candidate objects in patches could be generated and will be helpful with improving following ship detection. The proposed renormalization net was tested on a Google-Earth handcraft dataset. The experimental result demonstrates the proposed renormalization net greatly improve the ship detection with both of good detection accuracy and high IOU.
Identifying Impact Factors of Question Quality in Online Health Q&A Communities: an Empirical Analysis on MedHelp

Online health Q&A communities help patients, doctors and other users conveniently search and share healthcare information online and have gained much popularity all over the world. Good-quality questions that raise massive discussions could trigger users’ engagement online, which is beneficial for platform operation. However, little attention has been paid to the antecedents of question quality in online health Q&A communities. To have a deep investigation of healthcare question quality, this research aims to investigate the impact factors from two special aspects that are neglected in previous research, i
GekkoFS - A Temporary Distributed File System for HPC Applications

We present GekkoFS, a temporary, highly-scalable burst buffer file system which has been specifically optimized for new access patterns of data-intensive High-Performance Computing (HPC) applications. The file system provides relaxed POSIX semantics, only offering features which are actually required by most (not all) applications. It is able to provide scalable I/O performance and reaches millions of metadata operations already for a small number of nodes, significantly outperforming the capabilities of general-purpose parallel file systems.
RFUZZ: Coverage-Directed Fuzz Testing of RTL on FPGAs

Dynamic verification is widely used to increase confidence in the correctness of RTL circuits during the pre-silicon design phase. Despite numerous attempts over the last decades to automate the stimuli generation based on coverage feedback, Coverage Directed Test Generation (CDG) has not found the widespread adoption that one would expect. Based on new ideas from the software testing community around coverage-guided mutational fuzz testing, we propose a new approach to the CDG problem which requires minimal setup and takes advantage of FPGA-accelerated simulation for rapid testing. We provide test input and coverage definitions that allow fuzz testing to be applied to RTL circuit verification. In addition we propose and implement a series of transformation passes that make it feasible to reset arbitrary RTL designs quickly, a requirement for deterministic test execution. Alongside this paper we provide rfuzz, a fully featured implementation of our testing methodology which we make available as open-source software to the research community. An empirical evaluation of RFUZZ shows promising results on archiving coverage for a wide range of different RTL designs ranging from communication IPs to an industry scale 64-bit CPU.
Keystroke Biometrics for Freely Typed Text Based on CNN model

Keystroke biometrics, as an authentication method with advantages of no extra hardware cost, easy-to-integrate and high-security, has attracted much attention in user authentication. However, a mass of researches on keystroke biometrics have focused on the fixed-text analysis, while only a few took free-text analysis into consideration. And in the field of free-text analysis, most researchers usually devote their efforts to extracting the most appropriate keystroke features on their own experience. These methods were inevitably questionable due to their strong subjectivity. In this paper we proposed a multi-user keystroke authentication scheme based on CNN model, which can automatically figure out the appropriate features for the model, adjust and optimize the model constantly to further enhance the performance of model. In the experiment on a small sample set, the performance is improved more than 10% compared with the benchmark. Our model achieves an average recognition accuracy of 92.58%, with FAR of 0.24% and FRR of 7.34%.
Yolk vitamin E positively affects prenatal growth but not oxidative status in yellow-legged gull embryos

Abstract Parental effects occur whenever the phenotype of parents or the environment that they experience influences the phenotype and fitness of their offspring. In birds, parental effects are often mediated by the size and biochemical quality of the eggs in terms of maternally transferred components. Exogenous antioxidants are key egg components that accomplish crucial physiological functions during early life. Among these, vitamin E plays a vital role during prenatal development when the intense metabolism accompanying rapid embryo growth results in overproduction of pro-oxidant molecules. Studies of captive birds have demonstrated the positive effect of vitamin E supplementation on diverse phenotypic traits of hatchling and adult individuals, but its effects on embryo phenotype has never been investigated neither in captivity nor under a natural selection regime. In the present study, we experimentally tested the effect of the in ovo supplementation of vitamin E on morphological traits and oxidative status of yellow-legged gull (Larus michahellis) embryos. The supplementation of vitamin E promoted somatic growth in embryos soon before hatching, but did not affect their oxidative status. Our results suggest that maternally transferred vitamin E concentrations are optimized to prevent imbalances of oxidative status and the consequent raise of oxidative damage in yellow-legged gull embryos during prenatal development.
Combining Sentiment Analysis With a Fuzzy Kano Model for Product Aspect Preference Recommendation

Item-based collaborative filtering (ItemCF) is a proven algorithm in recommendation systems that is based on a neighborhood algorithm, but it neglects the influence of sentiment in different aspects. However, customers always express their opinion in reviews, and these personalized data will influence the recommendation effect. This paper proposes an aspect sentiment collaborative filtering algorithm (ASCF), which combines sentiment analysis with a fuzzy Kano model. ASCF obtains the users’ different attitudes toward aspects of the product by fine-grained sentiment analysis from the user’s purchase records, and then analyzes the user’s degree of desire and importance for each feature based on the fuzzy Kano model, proposing a novel similarity measure method with user preferences for a collaborative filtering algorithm. Experiments with Amazon data sets show that ASCF effectively improves the precision of ItemCF and opinion-enhanced collaborative filtering; it provides higher recommendation precision and fewer product recommendations at the similarity precision. The experiments use the smartphone catalog as an example to analyze the aspect-characteristic words distribution matrix.
GPU Acceleration of RSA is Vulnerable to Side-channel Timing Attacks

The RSA algorithm [21] is a public-key cipher widely used in digital signatures and Internet protocols, including the Security Socket Layer (SSL) and Transport Layer Security (TLS). RSA entails excessive computational complexity compared with symmetric ciphers. For scenarios where an Internet domain is handling a large number of SSL connections and generating digital signatures for a large number of files, the amount of RSA computation becomes a major performance bottleneck. With the advent of general-purpose GPUs, the performance of RSA has been improved significantly by exploiting parallel computing on a GPU [9], [18], [23], [26], leveraging the Single Instruction Multiple Thread (SIMT) model.
Commutable cold cathodes based on vertically aligned carbon nanotubes with a buried modulation electrode

A new design of commutable CNT emitters cathode with an on-chips gate has been investigated. Both fabrication and field emission proprieties of a buried gate cathode have been demonstrated. Current modulation by applying a small bias on the buried gate has been performed. This design shows excellent modulation properties and a high robustness.
Dual Millimeter Wave Reconfigurable Dielectric Lens Antenna

The objective of this paper is to present a directive hemispherical dielectric lens antenna, with reconfigurable frequency, operating in the millimeter wave ranges. Numerical results obtained from Ansys HFSS® software attest the structure functionality. The dielectric lens antenna is capable to reconfigure the operating band from 44 GHz to 75 GHz, with gain in the direction of maximum irradiation reaching 24.5 dBi.
A Fully Integrated 16-Channel Closed-Loop Neural-Prosthetic CMOS SoC With Wireless Power and Bidirectional Data Telemetry for Real-Time Efficient Human Epileptic Seizure Control

A 16-channel closed-loop neuromodulation system-on-chip (SoC) for human epileptic seizure control is proposed and designed. In the proposed SoC, a 16-channel neural-signal acquisition unit (NSAU), a biosignal processor (BSP), a 16-channel high-voltage-tolerant stimulator (HVTS), and wireless power and bidirectional data telemetry are designed. In the NSAU, the input protection circuit is used to prevent MOSFET from overstressing by the high-voltage stimulations. Hence, NSAUs can share electrodes with stimulators. The auto-reset chopper-stabilized capacitive-coupled instrumentation amplifiers (AR-CSCCIAs) are designed with the chopper-stabilized technique with a new offset reduction loop. The measured input-referred noise is 2.09 $\mu \textrm {V}_{\mathrm {rms}}$ and the noise-efficiency factor (NEF) is 3.78. The entropy-and-spectrum seizure detection algorithm is implemented in the BSP with 0.76-s seizure detection latency and 97.8% detection accuracy. When the seizure onset is detected by the BSP, the HVTS with adaptive supply control delivers 0.5–3-mA biphasic current stimulation to suppress the seizure onset. The proposed SoC is powered wirelessly, and the bidirectional data telemetry is realized through the same pair of coils in 13.56 MHz. The downlink data rate is 211 Kb/s with the binary phase-shift keying (BPSK) modulation and a new BPSK demodulator. The uplink data rate is 106 Kb/s with the load-shift keying (LSK) modulation. The proposed SoC is fabricated in a 0.18- $\mu \text{m}$ CMOS technology and occupies 25 mm2. Electrical tests have been performed to characterize the SoC performance. In vivo animal experiments using mini-pigs have been performed to successfully verify the closed-loop neuromodulation functions on epileptic seizure suppression.
A Numerical Method to Model Non-linear Damping Behaviour of Martensitic Shape Memory Alloys

This article investigates the efficiency of hybridizing composites with thin layers of martensitic shape memory alloys for improvement of damping. The non-linear damping behaviour of martensitic shape memory alloys is simulated using a modified version of Masing’s rules. The model was implemented in a user subroutine of a finite element code, and validated by a numerical simulation of experimental hysteresis loops at different maximum strain amplitudes. The experimental free decay of hybridized glass fiber reinforced polymer beams was simulated using the finite element model, including the validated model of the investigated materials. The amplitude-dependent damping of the hybrid beams in free decay was reproduced successfully in the numerical analysis and it was proven that the hybridization technique is efficient for improvement of damping.
The effectiveness of the use of Performance Management Systems: developing a framework for research

Recently, performance management systems (PMS) has been criticized by several authors. However, it is a fact that PMS have been functioned as a crucial and vital information system to control and manage organizational actions. The reasons why PMS are criticized are overlooking and unconsidered aspect of PMS: the use of PMS. Therefore, the purpose of the dissertation is to understand what is the use of PMS, and to reveal the positive and/or negative effects on managers and organizational performance through using PMS.
Stacked Encoder-Decoders for Accurate Semantic Segmentation of Very High Resolution Satellite Datasets

Semantic segmentation is currently a mainstream method for addressing several remote sensing applications, achieving recently remarkable performance by employing deep learning techniques. In particular, this is the case for pixel-wise dense classification models in very high resolution remote sensing datasets. In this paper, we exploit the use of a relatively deep architecture based on repetitive downscale-upscale processes that had been previously employed for human pose estimation tasks. By integrating such a model, we are aiming to capture and extract low-level details, such as small objects, object boundaries and edges. Experimental results and quantitative evaluation has been performed on the publicly available ISPRS (WGIII4) benchmark dataset indicating the potential of the proposed approach.
The Role of Formal and Informal Learning Systems in Virtual Learning Environments

Online learning systems are the norm in today’s educational institutions. However, students also use social networking applications for learning, and we seek to understand the use and effectiveness of such informal (social networking application based learning) systems on learning. We analyze the design characteristics of formal and informal learning systems, in an educational environment, to develop and test a model of their impact on learning outcomes. We find students attention, volume, velocity, access to information, and their degree of control over their use of the learning systems drives their learning success.
A Socio-Technical Modeling Framework for Designing Enterprise Capabilities

The need for flexible and adaptive IT is ever more pressing as enterprises compete in global digital economies and ecosystems. To enable flexibility and adaptability of IT, one requires tools and techniques that enable co-design of IT and business. Hence, this research builds on the strategic management literature, particularly the research on dynamic capabilities, to propose a socio-technical modeling framework for designing enterprise capabilities. An agent-oriented modeling framework for understanding social and technical requirements when designing enterprise capabilities is proposed by building on conceptual modeling practices and techniques. An overview of the research design consisting of objectives, questions, and methodology is presented in this paper.