Miracle-dz/UniFAR-FTUs huggingface dataset:
Framework for component model selection

Advances in component-oriented software development research have led, among others, to the birth of a large number of component models, which has caused difficulties to the software developers in selecting the suitable component models to be used. To overcome the difficulties, a framework for component model selection is proposed in this study. The framework allows software developer to specify the criteria of the component model that they would like to use and recommends the component model(s) that fits most of the criteria. An application of the framework shows that it has the potential to be used by the software developers in determining the suitable component models.
Focusing bistatic configuration SAR data using extended chirp scaling

For the bistatic synthetic aperture radar (BiSAR) data, the range cell migration (RCM) in the range-Doppler (RD) domain has not linear dependency on the bistatic range, and the residual errors of the linear range cell migration correction (RCMC) lead to severe image degradation, especially at the edges of the range swath, if not removed. This paper quantitatively analyzes the effect of the residual errors and presents a new bistatic algorithm using extended chirp scaling for focusing the bistatic translational invariant SAR data. Simulations show that the new algorithm can get better focusing performance very much.
The case for GPGPU spatial multitasking

The set-top and portable device market continues to grow, as does the demand for more performance under increasing cost, power, and thermal constraints. The integration of Graphics Processing Units (GPUs) into these devices and the emergence of general-purpose computations on graphics hardware enable a new set of highly parallel applications. In this paper, we propose and make the case for a GPU multitasking technique called spatial multitasking. Traditional GPU multitasking techniques, such as cooperative and preemptive multitasking, partition GPU time among applications, while spatial multitasking allows GPU resources to be partitioned among multiple applications simultaneously. We demonstrate the potential benefits of spatial multitasking with an analysis and characterization of General-Purpose GPU (GPGPU) applications. We find that many GPGPU applications fail to utilize available GPU resources fully, which suggests the potential for significant performance benefits using spatial multitasking instead of, or in combination with, preemptive or cooperative multitasking. We then implement spatial multitasking and compare it to cooperative multitasking using simulation. We evaluate several heuristics for partitioning GPU stream multiprocessors (SMs) among applications and find spatial multitasking shows an average speedup of up to 1.19 over cooperative multitasking when two applications are sharing the GPU. Speedups are even higher when more than two applications are sharing the GPU.
General or Vocational? Evidence on School Choice, Returns, and Sheep Skin Effects from Egypt 1998

The debate on the relative benefits of upper secondary vocational education and of general education is still going. While this area of research has been very active in the United States and industrialized countries in recent years, empirical work in the Middle East countries still lacking. This study adds new evidence to that debate. The paper identifies factors that influence an individual’s choice between vocational and general education in Egypt as well as the relative returns between them and beyond secondary levels. Using an ordered logit model, the study finds that an individual from a well educated family is more likely to undertake general education. The presence of young siblings in the household motivates the parents to send their daughters to vocational schooling, but not their sons. After correcting for self-selection, the study also finds vocational education to offer higher returns (earnings) than general secondary education does for men, but not for women. Sheepskin effect was also evident for men but not for women.
Mercuric Compounds Induce Pancreatic Islets Dysfunction and Apoptosis in Vivo

Mercury is a toxic heavy metal that is an environmental and industrial pollutant throughout the world. Mercury exposure leads to many physiopathological injuries in mammals. However, the precise toxicological effects of mercury on pancreatic islets in vivo are still unclear. Here, we investigated whether mercuric compounds can induce dysfunction and damage in the pancreatic islets of mice, as well as the possible mechanisms involved in this process. Mice were treated with methyl mercuric chloride (MeHgCl, 2 mg/kg) and mercuric chloride (HgCl2, 5 mg/kg) for more than 2 consecutive weeks. Our results showed that the blood glucose levels increased and plasma insulin secretions decreased in the mice as a consequence of their exposure. A significant number of TUNEL-positive cells were revealed in the islets of mice that were treated with mercury for 2 consecutive weeks, which was accompanied by changes in the expression of the mRNA of anti-apoptotic (Bcl-2, Mcl-1, and Mdm-2) and apoptotic (p53, caspase-3, and caspase-7) genes. Moreover, plasma malondialdehyde (MDA) levels increased significantly in the mice after treatment with mercuric compounds for 2 consecutive weeks, and the generation of reactive oxygen species (ROS) in the pancreatic islets also markedly increased. In addition, the mRNA expression of genes related to antioxidation, including Nrf2, GPx, and NQO1, were also significantly reduced in these islets. These results indicate that oxidative stress injuries that are induced by mercuric compounds can cause pancreatic islets dysfunction and apoptosis in vivo.
Simulation-based evaluation of techniques for privacy protection in VANETs

In vehicular ad hoc networks (VANETs) tracking of participants is an issue that is examined by many research groups. These groups came up with several different concepts of counter measures against tracking attacks. All of these presented techniques seem to offer a pretty good protection. We pick out two very promising concepts - the Mix Zones and the Silent Periods - to examine them in a simulation environment to actually identify their strengths and weaknesses. Our simulation results show rather high success rates for attackers with relatively unsophisticated attack heuristics. Furthermore we confirm the correlation between several influencing factors and the success rates of attacks and study the connection to the common metrics k-anonymity and entropy.
A Review of Mac Protocols for Wireless Body Area Networks

This paper represents broad study of MAC protocols built-up for wireless body area networks. In Today’s world data is constantly evolving to process larger data sets and maintain higher degree of connectivity. At same time, advances in efficiency allow for amplified mobility and openness. Body Area Networks signify the liable union among connectivity and smartness. A Body Area Network (BAN) is defined officially as a scheme of devices in close proximity to a person’s body that collaborate for the benefit of the user. The enhancement in average lifetime and fitness cost in many developed nations are the main reasons for innovation in health care. Various applications and requirements of the WBAN are discussed in his paper. Also, the strengths and weakness of the protocols have been discussed.
On the statistical distribution of processing times in network intrusion detection

Intrusion detection systems (IDSs) are relatively complex devices that monitor information systems in search for security violations. Characterizing the service times of network IDSs is a crucial step in improving their real time performance. We analyzed about 41 million packets organized in five data sets of 10 minutes each collected at the entry point of a large production network and processed by Snort, a commonly used IDS. The processing times of the three main stages in Snort were measured. The main conclusions of our study were: (1) rule checking accounts for about 75% of the total processing time in IDSs, with mean pay load checking time being 4.5 times larger than mean header checking time. (2) The distribution of rule checking limes is markedly bimodal, a direct consequence of the bimodality in packet composition in current high speed Internet traffic. (3) Header processing times have a small variance and small correlation coefficients. (4) In contrast, the distribution of payload processing times displays high variance, in a form that can be generally characterized as "slightly heavy-tailed". Explicitly, payload processing times have a lognormal upper tail, clipped at the top 1%. This extreme 1% upper tail is better fit by an exponential distribution. (5) Additionally, payload processing times were shown to be highly correlated, with correlation coefficients several orders of magnitude higher than the confidence bands for the standard whiteness test. The impact of these findings in the design of IDSs for real time operation in networks is discussed, and compared with existing results for processing times for Unix processes, which were shown to display pronounced heavy-tailed characteristics.
Information-theoretic lower bounds on the oracle complexity of sparse convex optimization

Relative to the large literature on upper bounds on complexity of convex optimization, lesser attention has been paid to the fundamental hardness of these problems. Recent years have seen a surge in optimization methods tailored to sparse optimization problems. In this paper, we study the complexity of stochastic convex optimization in an oracle model of computation, when the objective is optimized at a sparse vector in a high dimensional space. Our result is matched by an appropriately tuned method of mirror descent, establishing the minimiax optimality of the result.
Lumpings of Markov Chains, Entropy Rate Preservation, and Higher-Order Lumpability

A lumping of a Markov chain is a coordinatewise projection of the chain. We characterise the entropy rate preservation of a lumping of an aperiodic and irreducible Markov chain on a finite state space by the random growth rate of the cardinality of the realisable preimage of a finite-length trajectory of the lumped chain and by the information needed to reconstruct original trajectories from their lumped images. Both are purely combinatorial criteria, depending only on the transition graph of the Markov chain and the lumping function. A lumping is strongly k-lumpable, if and only if the lumped process is a kth-order Markov chain for each starting distribution of the original Markov chain. We characterise strong k-lumpability via tightness of stationary entropic bounds. In the sparse setting, we give sufficient conditions on the lumping to both preserve the entropy rate and be strongly k-lumpable.
Particle swarm optimization-based wavelet packet regression for multivariate analysis of near-infrared spectroscopy

Near infrared (NIR) spectroscopy, combined with multivariate calibration method, is a very important issue for qualitative and quantitative application. In pursuit of this aim, a new hybrid algorithm (PSO-WPLS) was proposed for multivariate regression model development. At first, wavelet packet transform (WPT) algorithm and its reconstruction algorithm are used to split the collected spectra into different frequency components. Then, to take advantages of multiscale property of NIR spectra, the useful WPT components are selected by the particle swarm optimization (PSO) algorithm coupled with a fitness function of prediction error. At last, each selected WPT components are introduced to regression models to develop a series of sub-models. The PSO-WPT model can be constructed through the involvement of all sub-models characterized by a series of weighted regression coefficient. To validate this algorithm, it was used to measure the oil concentration of corn samples. Compared with the conventional WPLS algorithm, the PSO-WPLS algorithm can significantly improve the quality of regression model with the prediction errors decreasing by up to 72.5%, meaning that it is a potential way for developing multivariate model with high precision.
Skeletal scintigraphy with technetium-99m-tetraphenyl porphyrin sulfonate for the detection and determination of osteomyelitis in an animal model.

UNLABELLED This article explores the accumulation of 99mTc-tetraphenyl porphyrin sulfonate (TPPS4) at inflammatory sites, especially osteomyelitis, and compares the results with 111In Cl3 and 111In-WBC in an animal model. METHODS Osteomyelitis was induced in 12 New Zealand white rabbits by injecting staphylococcus aureus in the left tibia. Three weeks later, radiographs confirmed the disease. Two hours later, after injection of 74 MBq 99mTc-TPPS4, scintiphotos of the lower extremities were acquired and repeat scintiphotos were obtained 24 hr after injection of 5.55 MBq 111In Cl3. After these studies, 24- and 48-hr scintiphotos of the lower extremities were acquired after injecting 5.55 MBq 111In-labeled WBC. RESULTS The left tibia averaged three times the uptake with 99mTc-TPPS4 compared with right tibia; with 111In Cl3 and 111In WBC the ratios are two times. These three radiopharmaceuticals reveal positive images, but the image quality using 99mTc-TPPS4 is better, as would be expected from the more favorable physical characteristics of 99mTc and the higher uptake. CONCLUSION The traditional combination of three-phase bone and 67Ga-citrate scintigraphy can be replaced by a single injection of 99mTc-TPPS4 with imaging as early as 2 hr. Finally, the use 99mTc-TPPS4 should result in a substantial reduction in radiopharmaceutical cost.
Sidescan sonar imagery segmentation with a combination of texture and spectral analysis

This paper deals with the seabed classification from textured sonar images and specially the potential of the combination of features extracted from co-occurrences matrices and directional filter bank (DFB) . The texture analysis based on the co-occurrences matrices is strongly dependant on the choice of parameter values (e.g. the distance and the angular direction for the estimation of the number of transitions). In most cases the choice is not trivial. To get representative features from textures with different spatial frequencies, a comprehensive set of co-occurrence matrices with corresponding displacements and orientation has to be computed. In this work, we investigate a non classical approach based on the DFB. The approach uses a decomposition of the Fourier spectrum into three spectral bands: low, medium and high frequencies. A subsequent analysis of the pattern isotropy is conducted by dividing the medium spectral band into small, overlapped, angular sectors. The features extracted from this process are assessed so as to determine their potential on the classification performances. First, a comparison with classification performances result given by texture features derived from grey level co-occurrences matrices (GLCM) is made. Finally the global performance of the segmentation is assessed using the spectral features, the features extracted from GLCM and the grazing angle. The Klein 5000 experimental data used in this study have been acquired by DGA/GESMA during BP 02 experiment conducted by NURC.
Optimal Investment and Consumption Decisions Under the Ho-Lee Interest Rate Model

In this paper, we consider an investment and consumption problem with stochastic interest rate, in which risk-free interest rate dynamics is driven by the Ho-Lee model,while risky asset price is supposed to follow a geometric Brownian motion and be correlated with interest rate dynamics. Our goal is to seek an optimal investment and consumption strategy to maximize the expected discounted utility of consumption and terminal wealth in the finite horizon. Firstly, we apply dynamic programming principle to derive Hamilton-Jacobi-Bellman(HJB) equation for the value function and take power utility and logarithm utility for our analysis. Secondly, by conjecturing the form of a solution and solving partial differential equations, we obtain the closed-form solutions to the optimal investment and consumption strategies. Finally, we provide a numerical example to demonstrate the impact of market parameters on the optimal investment and consumption strategy. Key–Words: Investment and consumption problem; the Ho-Lee model; dynamic programming principle; HJB equation; closed-form solution;
Less is More: Learning Prominent and Diverse Topics for Data Summarization

Statistical topic models efficiently facilitate the exploration of large-scale data sets. Many models have been developed and broadly used to summarize the semantic structure in news, science, social media, and digital humanities. However, a common and practical objective in data exploration tasks is not to enumerate all existing topics, but to quickly extract representative ones that broadly cover the content of the corpus, i.e., a few topics that serve as a good summary of the data. Most existing topic models fit exactly the same number of topics as a user specifies, which have imposed an unnecessary burden to the users who have limited prior knowledge. We instead propose new models that are able to learn fewer but more representative topics for the purpose of data summarization. We propose a reinforced random walk that allows prominent topics to absorb tokens from similar and smaller topics, thus enhances the diversity among the top topics extracted. With this reinforced random walk as a general process embedded in classical topic models, we obtain \textit{diverse topic models} that are able to extract the most prominent and diverse topics from data. The inference procedures of these diverse topic models remain as simple and efficient as the classical models. Experimental results demonstrate that the diverse topic models not only discover topics that better summarize the data, but also require minimal prior knowledge of the users.
On the Determinants of Pyramidal Ownership: Evidence on Dilution of Minority Interests

We investigate the determinants, costs and benefits of corporate affiliation to pyramidal holdings. We find that there is a mismatch between cash flow rights and voting rights in firms affiliated to pyramidal holdings, and that corporate policies reflect cash distribution preferences of the ultimate owners. We also find a permanent depressive effect of pyramidal ownership on corporate value. This finding is consistent with the hypothesis that the layers of equity holdings within the pyramidal holding contribute to form an impervious veil behind which ultimate owners engage in expropriating behavior. Our investigation of affiliated firms suggests that ultimate owners, mostly families, are adept at combining their cash flow rights, voting power, and distance from affiliates in a way that minimizes sensitivity to negative events and maximizes sensitivity to positive events.
Microarray background correction: maximum likelihood estimation for the normal–exponential convolution

Background correction is an important preprocessing step for microarray data that attempts to adjust the data for the ambient intensity surrounding each feature. The “normexp” method models the observed pixel intensities as the sum of 2 random variables, one normally distributed and the other exponentially distributed, representing background noise and signal, respectively. Using a saddle-point approximation, Ritchie and others (2007) found normexp to be the best background correction method for 2-color microarray data. This article develops the normexp method further by improving the estimation of the parameters. A complete mathematical development is given of the normexp model and the associated saddle-point approximation. Some subtle numerical programming issues are solved which caused the original normexp method to fail occasionally when applied to unusual data sets. A practical and reliable algorithm is developed for exact maximum likelihood estimation (MLE) using high-quality optimization software and using the saddle-point estimates as starting values. “MLE” is shown to outperform heuristic estimators proposed by other authors, both in terms of estimation accuracy and in terms of performance on real data. The saddle-point approximation is an adequate replacement in most practical situations. The performance of normexp for assessing differential expression is improved by adding a small offset to the corrected intensities.
GreenSensing: A Fine Grained Power Monitoring System for a Network of Computers

Recent studies have shown the necessity of fine grained power usage visibility to encourage user behavior energy conservation. Existing works toward this direction are mainly focused on residential monitoring scenarios. This study considers another important scenario of providing fine grained power usage information over a set of networked computers. Such scenario could be applied to a wide range of workspaces, including research labs in colleges and business offices in companies. To our best knowledge, no existing systems provide cost-effective solutions for such scenario. To this end, we present GreenSensing which provides real time individual power usage estimation by leveraging the fact that the amount of power usage is highly correlated to CPU usage rate of a computer. As CPU usage can be obtained by a software program, we can have the power information without real metering instruments, and therefore have the benefit of zero infrastructure cost. However, due to the indirectness, before the estimation works, a proper calibration for computers is required, which needs lots of human intervention. To this problem, we propose a novel framework that empowers GreenSensing to automatically and simultaneously calibrate multiple computers on the fly. We show through experiments GreenSensing provides only 4.1% to 6.2% estimation error on individual power usage.
Metabolic Pathway Modeling by Using the Nearest Neighbor Algorithm

A new computational approach was developed for modeling the metabolic pathways. The new approach is featured by combing the knowledge of gene ontology, microarray, and chemical functional group to formulate the enzyme-substrate/product couples in a 1,660 vector space. The nearest neighbor algorithm was used to perform the prediction of the networking relationship occurring in the metabolic pathways. The average overall success rate by jackknife cross-validation tests for the 79 metabolic pathways in the budding yeast system was over 94%, suggesting that the current approach might become a useful tool for studying metabolic pathways and many other networking-related areas.
Beamforming design for multi-pair two-way AF MIMO relaying networks using quadratic programming

In this paper, beamforming design for multi-pair two-way amplify-and-forward (AF) MIMO relaying networks is considered, in which there are multiple users using one multi-antenna relay to exchange information. As there are multiple user-pairs, beamforming matrix at relay should be carefully designed to eliminate the mutual interference among different user-pairs. Both signal-to-interference-plus-noise ratio (SINR) at the receiver and mean-square-error (MSE) of the detected data are taken as the design criteria. Therefore, two different algorithms are proposed based on a framework of quadratic programming. The performance of the two proposed algorithms are finally demonstrated by the simulation results.