Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability analysis of test cases for program slicing

The concept used in the research paper includes program slicing which is referred as the process of computing the chosen set of instructions of the program. The slicing criterion refers to the slice that is likely to affect the values at some point while executing the program. The paper highlights the different test cases and comparative analysis of program slicing methods which corresponds to the applications which are usually utilized in software modification activities i.e. Debugging process, testing analysis and its phases, calculating complexity and program understanding etc. In the approach used in the paper the “slicing” method of the program focus on precise & defined sub-components of a program; which can further be used to produce the instructions needed for the program, relevant to an explicit method of computation. Applying slicing techniques to software's using slicing tools, we can determine which components can be facilitated and reused. The process of designing phase of and the reused patterns are likely to suggest improvement in the respective code.
User-Level Fine-Grained Adaptive Real-Time Scheduling via Temporal Reflection

Real-time systems must adapt their behaviour when the timing assumptions they are based on change at run time. A viable approach leading to effective adaptations consists of exploiting application-specific knowledge, but limitations of ordinary schedulers constrain its applicability. In this paper this problem is tackled using a reflective scheduler, which enables a computing system to perform temporal reflection, that is to fully observe and control its own temporal behaviour. The scheduler is implemented for the L4 microkernel, and validated by solving a real-time image analysis problem. Compared with other approaches the reflective scheduler is orders of magnitude more precise, achieving microsecond-level accuracy, while its implementation is entirely at user-level, and it does not require any changes to be made to the microkernel itself
Super-Nyquist Rateless Coding for Intersymbol Interference Channels

A rateless transmission architecture is developed for communication over Gaussian intersymbol interference channels, based on the concept of super-Nyquist (SNQ) signaling. In such systems, the signaling rate is chosen significantly higher than the Nyquist rate of the system. We show that such signaling, when used in conjunction with good "off-the-shelf" base codes, simple linear redundancy, and minimum mean-square error decision feedback equalization, results in capacity-approaching, low-complexity rateless codes for the time-varying intersymbol-interference channel. Constructions for both single-input / single-output (SISO) and multi-input / multi-output (MIMO) ISI channels are developed.
Low-complexity DCT-domain video transcoders for arbitrary-size downscaling

In this paper, we propose efficient techniques and architectures for realizing spatial-downscaling transcoders in the DCT domain. We present efficient DCT-domain methods for arbitrary-size downscaling and upscaling. We show that, by integrating the downscaling process into the DCT-domain motion compensation (DCT-MC) operation for B-frames, the computation of DCT-MC and downscaling can be significantly reduced, leading to a simplified cascaded DCT-domain downscaling transcoder (CDDT) without introducing extra quality degradation. We also propose another scheme to further reduce the computation and storage cost which may introduce drifting errors. Experimental results show that the two proposed schemes can achieve significant computation reduction when compared with the original CDDT without any degradation or with introducing acceptable quality degradation, respectively.
Nanolaboratory - a prototype nanomanufacturing system

A nanolaboratory - a prototype nanomanufacturing system is presented, which is composed with a nanorobotic manipulation system with 4 units and 16 degrees-of-freedom (DOFs), a nano fabrication system based on electron-beam-induced deposition (EBID) with an internal or external precursor evaporation reservoir equipped with a thermal field emission electron source or a nanotube cold cathode, and a real-time observation and measurement system based on a field emission scanning electron microscope (FESEM) equipped with 3-4 conventional atomic force microscope (AFM) cantilevers, piezoresistive levers or nanotube probes. Nanotube devices including a mass flow sensor, a linear bearing, and nanotube scissors are fabricated in the nanolaboratory.
Analysis on the resolution of the different signals in an on-line handwritten signature verification system applied to portable devices

Nowadays, the use of portable devices with touch screens has reached an extended use among the population. With this technology, it is possible to incorporate to such devices the possibility of using handwritten signature to authenticate the user. In order to study the possibilities to carry on this authentication, a previous analysis has been carried out in this paper to evaluate how an already implemented on-line signature authentication algorithm will work in portable devices. Signature database signals have been re-scaled to emulate signals taken from a portable device, emulating two technologies, capacitive and resistive screens. For this, using only 3 of the 5 standard signals captured by graphic tablets: X, Y and P. Support Vector Machines have been used as an algorithm to test against the modified database, and results show good performance of the algorithms, obtaining rates around EER=3%, showing that the algorithms will give good results implemented in real smart phones.
Tactile camera vs. tangible camera: taking advantage of small physical artefacts to navigate into large data collection

This paper presents the design and evaluation of two interaction techniques used to navigate into large data collection displayed on a large output space while based on manipulations of a small physical artefact. The first technique exploits the spatial position of a digital camera and the second one uses its tactile screen. User experiments have been conducted to study and compare the both techniques, with regards to users' performance and satisfaction. Results establish that Tactile technique is more efficient than Tangible technique for easy pointing tasks while Tangible technique is better for hardest pointing tasks. In addition, users' feedback shows that they prefer to use the tangible camera, which requires fewer skills.
Oxidative Stress, Mitochondrial DNA Mutation, and Apoptosis in Aging

A wide spectrum of alterations in mitochondria and mitochondrial DNA (mtDNA) with aging has been observed in animals and humans. These include (i) decline in mitochondrial respiratory function; (ii) increase in mitochondrial production of reactive oxygen species (ROS) and the extent of oxidative damage to DNA, proteins, and lipids; (iii) accumulation of point mutations and large-scale deletions of mtDNA; and (iv) enhanced apoptosis. Recent studies have provided abundant evidence to substantiate the importance of mitochondrial production of ROS in aging. On the other hand, somatic mtDNA mutations can cause premature aging without increasing ROS production. In this review, we focus on the roles that ROS play in the aging-associated decline of mitochondrial respiratory function, accumulation of mtDNA mutations, apoptosis, and alteration of gene expression profiles. Taking these findings together, we suggest that mitochondrial dysfunction, enhanced oxidative stress, subsequent accumulation of mtDNA mutations, altered expression of a few clusters of genes, and apoptosis are important contributors to human aging.
Polly - Performing Polyhedral Optimizations on a Low-Level Intermediate Representation

The polyhedral model for loop parallelization has proved to be an effective tool for advanced optimization and automatic parallelization of programs in higher-level languages. Yet, to integrate such optimizations seamlessly into production compilers, they must be performed on the compiler's internal, low-level, intermediate representation (IR). With Polly, we present an infrastructure for polyhedral optimizations on such an IR. We describe the detection of program parts amenable to a polyhedral optimization (so-called static control parts), their translation to a Z-polyhedral representation, optimizations on this representation and the generation of optimized IR code. Furthermore, we define an interface for connecting external optimizers and present a novel way of using the parallelism they introduce to generate SIMD and OpenMP code. To evaluate Polly, we compile the PolyBench 2.0 benchmarks fully automatically with PLuTo as external optimizer and parallelizer. We can report on significant speedups.
Electronic knowledge sharing in oil and gas sector: A grounded theory approach

Employees’ lack of participation in electronic knowledge sharing (EKS) is a subject that has been attracted to both practitioners and academicians in the field of organization and information technology (IT). The organizations employ IT tools for knowledge sharing with the purpose to manage employees’ knowledge especially when they are geographically dispersed.However, the number of employees who participate for electronic knowledge sharing is still inadequate; most organizations still fail while few others are successful. With this circumstance, it is important for organization to get insight and understand to enhance the number of participation to electronic knowledge sharing.Therefore, the paper seeks to explore the concepts or tentative theories of employees’ perceptions and experiences about electronic knowledge sharing to explain the behavior of employees about electronic knowledge sharing.This paper will present the grounded theory (GT) approach to conceptualize the phenomenon of electronic knowledge sharing of oil and gas sector in Malaysia.
Prostate cancer-derived angiogenin stimulates the invasion of prostate fibroblasts

Prostate fibroblasts promote prostate cancer progression by secreting factors that enhance tumour growth and induce the migration and invasion of prostate cancer cells. Considering the role of fibroblasts in cancer progression, we hypothesized that prostate cancer cells recruit these cells to their vicinity, where they are most directly available to influence cancer cell behaviour. To test this hypothesis, we performed modified Boyden chamber assays assessing the migration and collagen I invasion of normal primary prostate fibroblasts (PrSCs) and prostate cancer‐associated fibroblasts (PCAFs) in response to media conditioned by the metastatic prostate cancer cell lines PC‐3, LNCaP and DU145. During 4‐hr incubations, PrSCs and PCAFs migrated and invaded in response to the conditioned media. To identify candidate proteins in the conditioned media that produced these effects, we performed cytokine antibody arrays and detected angiogenin in all three media. Angiogenin‐blocked PC‐3‐conditioned medium, obtained using an anti‐angiogenin polyclonal antibody or angiogenin siRNA, significantly reduced PC‐3‐induced PrSC and PCAF collagen I invasion. Furthermore, angiogenin alone at 1, 2 and 5 ng/ml significantly stimulated PCAF collagen I invasion. These results suggest that PC‐3‐derived angiogenin stimulates the invasion of normal prostate fibroblasts and PCAFs and is sufficient for invasion of the latter. Because prostate fibroblasts play key roles in prostate cancer progression, targeting their invasion using an anti‐angiogenin‐based therapy may be a strategy for preventing or treating advanced prostate cancer.
A Time-Position Join Method for Periodicity Mining in Time Series Databases

Periodicity mining is used for predicting trends intime series data. There are many applications data includingtemperature, stock prices depicted in the financial market, gene expression data analysis, etc. In general, there are threetypes of periodic patterns which can be detected in the timeseries data: (1) symbol periodicity, (2) sequence periodicityor partial periodic patterns, and (3) segment or full-cycleperiodicity. Rasheed et al. have proposed a two-phasesapproach to periodicity mining. In the first phase, they usethe suffix tree to produce candidate period patterns of allthree types of periodicity in a single run. However, we findthat those suffix-tree-related data structures are stillinefficient in generating candidates of period patterns. Therefore, in this paper, we use the following method forperiodicity mining in time series databases. On the design ofPhase 1 for generation of candidate patterns, we present ourtime-position join method. From the simulation results, weshow that our method is more efficient than their algorithm.
Buried object localization using direct algorithm

This study deals with buried object localization using a towed flexible array of sensors. So, the phases of the received signals deviate from the assumed plane wave model by the phase errors which are due to the random distortions of the antenna. In this letter, an objective function is defined and a new source steering vector including the acoustic scattering model at each sensor is proposed. The robust DIRECT (DIviding RECTangles) method is applied to minimize this objective function for estimating the bearings and the ranges of objects together with the phase errors. To decorrelate the received signals and to estimate the coherent signal subspace a fast focusing operator is proposed . The performance of the developed method is investigated on experimental data recorded during underwater acoustic experiments.
The Use of Convection-Enhanced Delivery with Liposomal Toxins in Neurooncology

Liposomes have long been effective delivery vehicles for transport of toxins to peripheral cancers. The combination of convection-enhanced delivery (CED) with liposomal toxins was originally proposed to circumvent the limited delivery of intravascular liposomes to the central nervous system (CNS) due to the blood-brain-barrier (BBB). CED offers markedly improved distribution of infused therapeutics within the CNS compared to direct injection or via drug eluting polymers, both of which depend on diffusion for parenchymal distribution. This review examines the basis for improved delivery of liposomal toxins via CED within the CNS, and discusses preclinical and clinical experience with these therapeutic techniques. How CED and liposomal technologies may influence future neurooncologic treatments are also considered.
Choosing between value and growth in mutual fund investing

This article informs investors on the choice between value and growth mutual funds. The well- established value premium demonstrates that, on average, value securities outperform growth secu- rities, suggesting that an investor may be wise to choose value funds. Extant studies, however, suggest that growth funds outperform value funds. We show that value funds indeed outperform growth funds especially in terms of lower realized risk and higher realized terminal wealth, leading us to recom- mend value funds over growth funds. We argue that previous findings result from a bias against value in some multifactor models.
A Role for the Ubiquitin–Proteasome System in Activity-Dependent Presynaptic Silencing

Chronic changes in electrical excitability profoundly affect synaptic transmission throughout the lifetime of a neuron. We have previously explored persistent presynaptic silencing, a form of synaptic depression at glutamate synapses produced by ongoing neuronal activity and by strong depolarization. Here we investigate the involvement of the ubiquitin–proteasome system (UPS) in the modulation of presynaptic function. We found that proteasome inhibition prevented the induction of persistent presynaptic silencing. Specifically, application of the proteasome inhibitor MG-132 (carbobenzoxy-l-leucyl-l-leucyl-l-leucinal) prevented decreases in the size of the readily releasable pool of vesicles and in the percentage of active synapses. Presynaptic silencing was accompanied by decreases in levels of the priming proteins Munc13-1 and Rim1. Importantly, overexpression of Rim1α prevented the induction of persistent presynaptic silencing. Furthermore, strong depolarization itself increased proteasome enzymatic activity measured in cell lysates. These results suggest that modulation of the UPS by electrical activity contributes to persistent presynaptic silencing by promoting the degradation of key presynaptic proteins.
Short-Term Traffic Flow Forecasting Using Macroscopic Urban Traffic Network Model

Traffic flow forecasting provides important information for both traffic control and traffic guidance. It should be both quick and accurate. A short-term traffic flow forecasting method is given based on the macroscopic urban traffic network model. The model is established to describe the substantial mechanism of traffic flow movement and the topology of the entire urban traffic network. It can simulate the traffic movement in the urban traffic network, and forecast the traffic flow states in the near future accurately. Also, the method has a good real-time feature due to the macroscopic model. In the simulation experiment, microscopic model CORSIM is used as the practical traffic system, and the proposed method is used to forecast the traffic flow of it. The simulation results show that the method has a good forecasting effectiveness.
Spectral Collocation and Waveform Relaxation Methods with Gegenbauer Geconstruction for Nonlinear Conservation Laws

Abstract We investigate the Chebyshev spectral collocation and waveform relaxation methods for nonlinear conservation laws. Waveform relaxation methods allow to replace the system of nonlinear differential equations resulting from the application of spectral collocation methods by a sequence of linear problems which can be effectively integrated by highly stable implicit methods. The obtained numerical solution is then enhanced on the intervals of smoothness by the Gegenbauer reconstruction. The effectiveness of this approach is illustrated by numerical experiments.
Aggregation and the Estimated Effects of Local Economic Conditions on Health

This paper considers the relationship between local economic conditions and health with a focus on different approaches to geographic aggregation. After reviewing the tradeoffs associated with more- and less-disaggregated analyses – including an investigation of the migratory response to changing economic conditions – I update earlier state-level analyses of mortality and infant health and then consider how the estimated effects vary when the analysis is conducted at differing levels of geographic aggregation. This analysis reveals that more-disaggregated analyses severely understate the extent to which downturns are associated with improved health. Further investigation reveals that county economic conditions have an independent effect on mortality but that state and regional economic conditions are stronger predictors. I also leverage county-level data to explore heterogeneity in the link between county economic conditions and health across states, demonstrating that local downturns lead to the greatest improvements in health in low-income states.
Towards online application of wireless EEG-based open platform Brain Computer Interface

Brain Computer Interface (BCI) is a system that directly utilize Electroencephalograph (EEG) signals to control external devices without aid from any limb of the body. BCI system consists of brainwave acquisition, signal processing, feature extraction and classification. A design of BCI system has been developed by using a wireless EEG Emotiv EPOC neuroheadset and OpenViBE. Both of them are open-source system which gives opportunity to develop our BCI system freely. Mu wave is extracted from the acquired brainwaves when the subject imagined hand movement. Mu wave can be obtained on FC5 and FC6, where premotor activities take place, by apply it to a 8 - 13 Hz bandpass filter. Mu wave power which is the square of EEG signal amplitude is extracted to be classified into two different classes. Feature classification is done by using Support Vector Machine (SVM) in offline classification and online training. EEG signal was acquired on three healthy subjects without well training with BCI control. The task of subjects are imaginary movement of right and left hand with stimulation by a left and right arrow on the screen. Configuration for training and testing phase has been successfully done in OpenViBE towards online application. The mean recognition rate in offline testing and single trial classification is 60.63% for right arrow and 45.93% for left arrow on all subjects.
Practical Experiences with real-world systems: Security in the world of reliable and safe systems

Reliability and Safety have always been associated to Safety Critical Systems. Since the failure of a Safety Critical System may lead to loss of human lives or large economical effects, the standards that guide the development of these systems have always focused in these two aspects, independently of the domain applicable. By looking into Reliability and Safety independently and focused, one can design a system highly reliable and safe without Security concerns. However, Security plays a major role in the achievement of both Reliability and Safety. A system cannot be reliable and safe if it is not secure. Therefore, the current processes to certify a Safety Critical System also address Security aspects, together with Reliability and Safety. This work presents the activities that have been performed in the scope of the certification of a Safety Critical System in the railway domain and how Security is tackled without jeopardizing Reliability and Safety. The data collected and its importance for guaranteeing safety, reliability and security is presented and discussed. A relationship between the activities performed and the standards concerns is established and examples of architecture decisions that could provide more Reliability and Safety but less Security will be presented.
Stretchy multivariate polynomial classification

A stretchy classification methodology adopting multivariate polynomials is proposed in this paper. Through minimization of an approximated p-norm of the parameter vector subject to classification error constraints, an approximated minimum norm solution in dual form is derived for under-determined systems. This is subsequently transformed into its primal form for over-determined systems. Practical feasibility of the proposed solution is illustrated by an evaluation on synthetic data as well as an application on benchmark real-world data.