Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Modified 16×16 Quantization Table Steganography on Colour Images

Steganography is the art of concealing information in ways that prevent the detection of hidden messages. Steganography includes an array of secret communication methods that hide the message from being seen or discovered. The paper presents a novel steganographic method based on the JPEG quantization table modification. Instead of dividing cover image into 8×8 blocks, the cover image is divided into nonoverlapping blocks of 16×16 pixels to embed secret information. Here we have considered colour images and investigated the feasibility of data hiding. Three performance parameters namely Capacity, MSE and PSNR have been compared on different sizes of standard test images. In comparison with Jpeg-Jsteg and Chang et al. methods based on the conventional blocks of 8x8 pixels the proposed method shows high performance with regard to embedding rate and PSNR of stego image.Furthermore, the produced stego-images are almost identical to the original cover images.
A Taxonomy of Software-Defined Networking (SDN)-Enabled Cloud Computing

Software-Defined Networking (SDN) opened up new opportunities in networking with its concept of the segregated control plane from the data-forwarding hardware, which enables the network to be programmable, adjustable, and reconfigurable dynamically. These characteristics can bring numerous benefits to cloud computing, where dynamic changes and reconfiguration are necessary with its on-demand usage pattern. Although researchers have studied utilizing SDN in cloud computing, gaps still exist and need to be explored further. In this article, we propose a taxonomy to depict different aspects of SDN-enabled cloud computing and explain each element in details. The detailed survey of studies utilizing SDN for cloud computing is presented with focus on data center power optimization, traffic engineering, network virtualization, and security. We also present various simulation and empirical evaluation methods that have been developed for SDN-enabled clouds. Finally, we analyze the gap in current research and propose future directions.
Decomposition of polarimetric synthetic aperture radar backscatter from upland and flooded forests

Abstract The goal of this research was to decompose polarimetric Synthetic Aperture Radar (SAR) imagery of upland and flooded forests into three backscatter types: single reflection, double reflection, and cross-polarized backscatter. We used a decomposition method that exploits the covariance matrix of backscatter terms. First we applied this method to SAR imagery of dihedral and trihedral corner reflectors positioned on a smooth, dry lake bed, and verified that it accurately isolated the different backscatter types. We then applied the method to decompose multi-frequency Jet Propulsion Laboratory (JPL) airborne SAR (AIRSAR) backscatter from upland and flooded forests to explain scattering components in SAR imagery from forested surfaces. For upland ponderosa pine forest in California, as SAR wavelength increased from C-band to P-band, scattering with an odd number of reflections decreased and scattering with an even number of reflections increased. There was no obvious trend with wavelength for cross-po.
THE BODY COMPOSITION AND SOME CONDITIONAL FEATURES OF WOMEN JUDOISTS OF THE TURKISH NATIONAL TEAM UDC 796 . 853 . 3 ( 560 )

In this study, which aimed to evaluate the body composition and some conditional features of the women judoists of the Junior National Team who took part in the 2009 World Championship, Anthropometric (Femur and Humerus Bi-epicondylar diameter, Flexed arm and calf girth, skinfold measurements etc.) and conditional tests (vertical jumping, anaerobic power, right and left hand grip strength) were applied on 19 athletes (age: 20.58±1.87 years, weight: 65.32±21.13 kg and height: 165.26±6.17 cm). Holtain brand calipers (Holtain Ltd, Crosswell, Crymych, UK) were used for the measurement of the diameter and thickness of skin curves, Takeı brand (Takeı Physical Fitness Test, T.K.K.5106, Made in PRC) jump meter and dynamometer were used for the measurements of the vertical jump and handling power. The somatotype was detected by Heath-Carter's Anthropometric method. The data gathered were analyzed by SPSS 15.0 packet program. Respectively, the BMI; 23.57±5.85 kg/m2, WHR; 0.76±0.06, RPI; 41.70±2.54 cm/kg0.333 , body fat percentage 13.26±3.22 %, femur bi-epicondylar diameter 8.12±1.04 cm, Humerus bi-epicondylar diameter 5.04±0.51 cm, calf girth 35.68±5.28 cm, flexed arm girth 30.26±5.36 cm. , vertical jump height 40.44±3.72 cm. , anaerobic power 91.07±26.75 kgm/sn, right and left hand grip strength were found 35.16±5.11 kg and 33.54±5.22 kg. The endomorphy, mesomorphy and ectomorphy of our athletes were found to be 3.36, 3.19 and 2.06 respectively. Similarities were observed between the gathered data and the data on our athletes.
An Ensemble Kalman Filter Implementation Based on Modified Cholesky Decomposition for Inverse Covariance Matrix Estimation

This paper develops an efficient implementation of the ensemble Kalman filter based on a modified Cholesky decomposition for inverse covariance matrix estimation. This implementation is named EnKF-MC. Background errors corresponding to distant model components with respect to some radius of influence are assumed to be conditionally independent. This allows to obtain sparse estimators of the inverse background error covariance matrix. The computational effort of the proposed method is discussed and different formulations based on various matrix identities are provided. Furthermore, an asymptotic proof of convergence with regard to the ensemble size is presented. In order to assess the performance and the accuracy of the proposed method, experiments are performed making use of the Atmospheric General Circulation Model SPEEDY. The results are compared against those obtained using the local ensemble transform Kalman filter (LETKF). Tests are performed for dense observations ($100\%$ and $50\%$ of the model components are observed) as well as for sparse observations (only $12\%$, $6\%$, and $4\%$ of model components are observed). The results reveal that the use of modified Cholesky for inverse covariance matrix estimation can reduce the impact of spurious correlations during the assimilation cycle, i.e., the results of the proposed method are of better quality than those obtained via the LETKF in terms of root mean square error.
Effect of Culture Medium in Callogenesis from Adult Walnut Leaves (Juglans regia L.)

Todefinepreliminaryaspectsofwalnut�(Juglans regia L.) callogenesis in indirect propagation systems, this work analyzed callogenic induction in adult leaves of walnut in broadleaf trees (BTM), Murashige and Skoog (MS) Driver Kuniyuki Walnut (DKW) and Woody Plant (WPM) media. Leaf segments of 1 cm 2 , previously sterilized, were placed in each culture medium, supplemented with 21.4 µM naphtalene acetic acid (NAA) and 8.8 µM 6-benzylaminopurine (BAP). A completely random design was used; the experimental unit was a Petri dish with six callus explants. Each treatment was composed of seven Petri dishes. Callogenesis (%), nodular calli (%) and histology of calli explants were evaluated at 4 weeks by staining 10 µm slices with safranine-fastgreen. There were nosignificantdifferences�(α�=�0.05)�incallogenesisamongevaluatedmedia;�butnodularcallipercentageswere� significantlyhigherinBTM�(75%)�andWPM�(63%). �OnlyincalliobtainedintheBTMmediumdidthehistology� show the presence of meristematic zones, but without external expression. These results indicate that the medium influencesthemorphogeniccharacteristicsofresultantcallus. �CalliproducedinBTMpresentedbettermorphogenic� potential, with meristems at primary stages of organization.
Effect of high‐intensity resistance circuit‐based training in hypoxia on aerobic performance and repeat sprint ability

Recent acute studies have shown that high‐intensity resistance circuit‐based (HRC) training in hypoxia increases metabolic stress. However, no intervention studies have yet proven their effectiveness. This study aimed to analyze the effect of 8 weeks of HRC in hypoxia on aerobic performance, resting energy expenditure (REE), repeat sprint ability (RSA) and hematological variables. Twenty‐eight subjects were assigned to hypoxia (FiO2 = 15%; HRChyp: n = 15; age: 24.6 ± 6.8 years; height: 177.4 ± 5.9 cm; weight: 74.9 ± 11.5 kg) and normoxia (FiO2 = 20.9%; HRCnorm: n = 13; age: 23.2 ± 5.2 years; height: 173.4 ± 6.2 cm; weight: 69.4 ± 7.4 kg) groups. Each training session consisted of two blocks of three exercises (Block 1: bench press, leg extension, front pull down; 2: deadlift, elbow flexion, ankle extension). Each exercise was performed at 6 repetitions maximum. Participants exercised twice weekly for 8 weeks and before and after the training program blood test, REE, RSA and treadmill running test were performed. Fatigue index in the RSA test was significantly decreased in the HRChyp (−0.9%; P < .01; ES = 2.75) but not in the HRCnorm. No changes were observed in REE and hematological variables. Absolute (4.5%; P = .014; ES = 0.42) and relative (5.2%; P = .008; ES = 0.43) maximal oxygen uptake (VO2max), speed at VO2max (4%; P = .010; ES = 0.25) and time to exhaustion (4.1%; P = .012; ES = 0.26) were significantly increased in HRChyp but not in the HRCnorm. No significant differences between groups were found. Compared with normoxic conditions, 8 weeks of HRC training under hypoxic conditions efficiently improves aerobic performance and RSA without changes in REE and red blood O2‐carrying capacity.
An attempt to assess the influence of road network on flash flood wave parameters-the case study of the Carpathian Foothills

The road network complements the natural drainage network and accelerates the water cycle in the catchment. In this study the methodology enabling for inclusion of a road network into a simple rainfall-runoff routing model was proposed. The road network was included into the surface drainage system and quantified as the Horton’s ratios. The bifurcation (RB), the length (RL) and area (RA) ratios increased when the road network was introduced to the surface drainage system. The Soil Conservation Service-Curve Number (SCS-CN) and Geomorphological Unit Hydrograph (GIUH) models were adapted to evaluate the influence of a road network on the flood wave hydrograph in the Zalasówka catchment (the Carpathian Foothills). Results revealed that the maximum flow is c.a. 20% higher, when the Horton’s ratios include the natural river system and the road network. Moreover, the differences between the simulated and observed maximum flows were lower when the RB, RL, and RA ratios included the road network as an element of the drainage system. It seems that including the road network in Horton’s ratios improves the simulation of flash flood wave parameters in small catchments.
Burnout and depressive symptoms are not primarily linked to perceived organizational problems

ABSTRACT In this 257-participant study (76% female; mean age: 44.84), we examined two ideas that are widespread among burnout researchers: (a) the idea that burnout is primarily related to occupational-level factors; and (b) the idea that burnout should be considered a sentinel indicator in research on negative occupational outcomes. We investigated the links between burnout and a series of generic and work-related variables, namely, depressive symptoms, neuroticism, extraversion, effort-reward imbalance in the job [ERI], social support at work (SSW), and turnover intention. Burnout was assessed with the Shirom-Melamed Burnout Measure, depressive symptoms with the PHQ-9, neuroticism and extraversion with the NEO-Five Factor Inventory, ERI with the 10-item version of the Effort-Reward Imbalance Questionnaire, SSW with the Job Content Questionnaire, and turnover intention with a dedicated 3-item measure. Correlation, multiple regression, and relative weight analyses were conducted. Burnout was not found to be more strongly linked to organizational and work-contextualized variables than to personality traits. In addition, turnover intention was not associated to a greater extent with burnout than with ERI. Burnout and depressive symptoms were highly correlated and exhibited overlapping nomological networks. Overall, our findings question the way burnout has been generally conceived.
Does Tail Label Help for Large-Scale Multi-Label Learning?

Large-scale multi-label learning (LMLL) annotates relevant labels for unseen data from a huge number of candidate labels. It is perceived that labels exhibit a long tail distribution in which a significant number of labels are tail labels. Most previous studies consider that the performance would benefit from incorporating tail labels. Nonetheless, it is not quantified how tail labels impact the performance. In this article, we disclose that whatever labels are randomly missing or misclassified, the impact of labels on commonly used LMLL evaluation metrics (Propensity Score Precision (PSP)@ $k$ and Propensity Score nDCG (PSnDCG)@ $k$ ) is directly related to the product of the label weights and the label frequencies. In particular, when labels share equal weights, tail labels impact much less than common labels due to the scarcity of relevant examples. Based on such observation, we propose to develop low-complexity LMLL methods with the goal of facilitating fast prediction time and compact model size by restraining less performance-influential labels. With the consideration that discarding labels may cause the loss of predictive capability, we further propose to preserve dominant model parameters for the less performance-influential labels. Experiments clearly justify that both the prediction time and the model size are significantly reduced without sacrificing much predictive performance.
Comparison of Spirometrical Variables in Patients with Asthma and COPD

Introduction and background: COPD is characterized by airflow limitation and the diagnosis is suggested by history and physical examination and is confirmed by spirometry where there is low FEV1 level where there is no significant reversibility in respond to bronchodilators. Asthma is an inflammatory disease also characterized by airflow limitation, but in contrast with COPD, the airflow limitation is highly reversible either with therapy or spontaneously. The residual volume and total lung capacities are increased in most cases. Individuals with COPD have more frequent acute chest illnesses that invariably decrease lung function for at least 3 months. Asthma is a chronic inflammatory disorder causing recurrent episodes of variable degrees of wheezing, dyspnea, chest tightness, and cough. The inflammation also causes an associate increase in the existing bronchial hyper responsiveness to different stimuli. Aim: To compare the spirometry results in patients with COPD and asthma. Method: Twenty subjects (ten with COPD and ten with asthma) underwent pulmonary function test (spirometry, DLCO [in COPD patients]). The study was non experimental and observational study where ten patients were taken each for COPD and asthma in non-randomized manner. Results: COPD subjects had increased TLC % , RV/TLC% along with increased severity of airflow limitation(decreased FEV1%,FVC%, FEV1/FVC%) and no significant reversibility in post bronchodilator test. Asthmatics had near normal FVC (so DLCO not done), and reduced FEV1%, FEV1/FVC%. Post bronchodilator reversibility was not significant for all subjects but diagnosis was done on the basis of family history and symptoms. Conclusion: Overall lung function is poorer in COPD than asthma in spite of the lesser duration of illness in the former.
Deep Reinforcement Learning of Marked Temporal Point Processes

In a wide variety of applications, humans interact with a complex environment by means of asynchronous stochastic discrete events in continuous time. Can we design online interventions that will help humans achieve certain goals in such asynchronous setting? In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous stochastic discrete events characterized using marked temporal point processes. In doing so, we define the agent's policy using the intensity and mark distribution of the corresponding process and then derive a flexible policy gradient method, which embeds the agent's actions and the feedback it receives into real-valued vectors using deep recurrent neural networks. Our method does not make any assumptions on the functional form of the intensity and mark distribution of the feedback and it allows for arbitrarily complex reward functions. We apply our methodology to two different applications in personalized teaching and viral marketing and, using data gathered from Duolingo and Twitter, we show that it may be able to find interventions to help learners and marketers achieve their goals more effectively than alternatives.
Performance Analysis Of Different Model Architectures Utilized In An Adaptive Neuro Fuzzy Inference System For Contraction Scour Prediction

The processes involved in the local scour due flow contraction are so complex that it is difficult to establish a general empirical analytical model to provide accurate estimation of scour. In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting equilibrium contraction scour depth in alluvial channels was investigated. The main subject of this work is to design an appropriate neural network architecture for training the ANFIS from a given set of input and output data. The training algorithms used in this work are (1) basic hybrid method, (2) basic backpropagation with gradient descent method, (3) backpropagation with momentum method, and (4) backpropagation with Levenberg-Marquardt method. Applying a self-developed software, the numerical experiments were carried out by combining these training algorithms with different ANFIS structures. Statistical indices of model performance such as mean average error, root mean squared error, and coefficient of correlation were measured for each combination. The results showed that among all given models the zero order Takagi-Sugeno’s model with four bell-shaped membership functions for each input and the Levenberg-Marquardt algorithm for training provided the best performance for estimating of contraction scour depth.
WHIPPET: a collaborative software environment for medical image processing and analysis

While there are many publicly available software packages for medical image processing, making them available to end users in clinical and research labs remains non-trivial. An even more challenging task is to mix these packages to form pipelines that meet specific needs seamlessly, because each piece of software usually has its own input/output formats, parameter sets, and so on. To address these issues, we are building WHIPPET (Washington Heterogeneous Image Processing Pipeline EnvironmenT), a collaborative platform for integrating image analysis tools from different sources. The central idea is to develop a set of Python scripts which glue the different packages together and make it possible to connect them in processing pipelines. To achieve this, an analysis is carried out for each candidate package for WHIPPET, describing input/output formats, parameters, ROI description methods, scripting and extensibility and classifying its compatibility with other WHIPPET components as image file level, scripting level, function extension level, or source code level. We then identify components that can be connected in a pipeline directly via image format conversion. We set up a TWiki server for web-based collaboration so that component analysis and task request can be performed online, as well as project tracking, knowledge base management, and technical support. Currently WHIPPET includes the FSL, MIPAV, FreeSurfer, BrainSuite, Measure, DTIQuery, and 3D Slicer software packages, and is expanding. Users have identified several needed task modules and we report on their implementation.
Teamwork Situated in Multiteam Systems: Key Lessons Learned and Future Opportunities

Many important contexts requiring teamwork, including health care, space exploration, national defense, and scientific discovery, present important challenges that cannot be addressed by a single team working independently. Instead, the complex goals these contexts present often require effectively coordinated efforts of multiple specialized teams working together as a multiteam system (MTS). For almost 2 decades, researchers have endeavored to understand the novelties and nuances for teamwork and collaboration that ensue when teams operate together as “component teams” in these interdependent systems. In this special issue on the settings of teamwork, we aim to synthesize what is known thus far regarding teamwork situated in MTS contexts and offer new directions and considerations for developing, maintaining, and sustaining effective collaboration in MTSs. Our review of extant research on MTSs reveals 7 key lessons learned regarding teamwork situated in MTSs, but also reveals that much is left to learn about the science and practice of ensuring effective multiteam functioning. We elaborate these lessons and delineate 4 major opportunities for advancing the science of MTSs as a critical embedding context for collaboration and teamwork, now and in the future.
A learning-based automatic clinical organ segmentation in medical images

Image segmentation plays an important role in medical image analysis and visualization since it greatly enhances the clinical diagnosis. Although many algorithms have been proposed, it is challenging to achieve an automatic clinical organ segmentation which requires speed and robustness. Automatically segmenting cardiac Magnetic Resonance Imaging (MRI) image is extremely challenging due to the artifacts of cardiac motion and characteristics of MRI. Moreover many of the existing algorithms are specific to a particular view of cardiac MRI images. We proposed a generic view-independent, learning-based method to automatically segment cardiac MRI images, which uses machine learning techniques and the geometric shape information. A main feature of our contribution is the fact that the proposed algorithm can use a training set containing a mix of various views and is able to successfully segment any given views. The proposed method consists of four stages. First, we partition the input image into a number of image regions based on their intensity characteristics. Then, we calculate the pre-selected feature descriptions for each generated region and use a trained classi.er to learn the conditional probabilities for every pixel based on the calculated features. In this paper, we use the Support Vector Machine (SVM) to train our classifier. The learned conditional probabilities of every pixel are then fed into an energy function to segment the input image. We optimize our energy function with graph cuts. Finally, domain knowledge is applied to verify the segmentation. Experimental results show that this method is very efficient and robust with respect to image views, slices and motion phases. The method also has the potential to be imaging modality independent as the proposed algorithm is not specific to a particular imaging modality.