Miracle-dz/UniFAR-FTUs huggingface dataset:
A MEASUREMENT MODEL OF THE FUNCTIONAL SIZE OF SOFTWARE MAINTAINABILITY REQUIREMENTS

The European ECSS-E-40 standard for the aerospace industry includes maintainability as one of sixteen non-functional requirements for the embedded and real time software. The software maintainability requirements measured internally and externally. According to the ECSS European standards, maintainability requirements are apportioned to set maintainability requirements for lower level products to conform to the maintenance concept and maintainability requirements of the system and the maintainability analysis shall identify the maintainability critical items. This paper propose a new measurement model of the functional size of maintainability requirements of software. This functional size of the maintainability requirements measured using the concepts of the ISO19761: COSMIC standard at an early phase of the software development life cycle. It used as one of the primary inputs for the software effort estimation process. Further, this paper presents the design of software standard etalon to help in development of software products more effectively. An experiment is conducted to verify the applicability of the proposed measurement model to measure the functional size of requirements specifications of an online library software.
Mutual Effects between Innovation Commitment and Exports: Evidence from the Owner-Manager in Colombia

Latin America and the Caribbean is recognized as a region of entrepreneurs, as evidenced by the high amount of ventures per capita; however, just a few number of companies export and most of them show a weak growth and little innovation. Using information from the GEM database – Colombia 2010 and 2011, this study investigates the mutual effects between the owner-manager’s commitment to innovation and the export behavior of companies. Logistic and Ordinal regression models are used to test the proposed hypotheses in this study. The main findings of this study suggest that the owner-manager’s commitment to innovation is an important booster for exports, as well as for increasing sales abroad. Furthermore, the results suggest that the export propensity, in turn, increases the likelihood of involvement of owner-managers to innovate in Colombia.
Requirements Engineering for Agile development of Safety-Critical Systems

Driven by the urge to be more flexible, quicker and still have high quality, large-scale companies managing safety-critical sytems are transitioning to agile development methodologies in their day-to-day software and systems development practices. Due to the standards and regulations requirements of the safety-critical systems development, full adoption of agile methods is not viable. Also the requirements engineering process, which is critical for successful development of safety-critical systems, is seen as anti-agile due to heavy upfront analysis. Thus, the use of hybrid approaches is the common practice. However, the overall picture of safetycritical systems development (especially in terms in communication and documentation) within such hybrid, agile, or other continuous software development environments is missing. This work describes a research agenda to address this challenge. The work proposes a series of empirical studies to discover the information needs and related knowledge, pertinent to product development. We expect to make a contribution by establishing guidelines and frameworks that can be used to make requirements engineering a foundation for agile systems development, while providing empirical evidence of the use of agile methods in safety-critical development.
Hydrolysis of empty fruit bunches of palm oil (Elaeis guineensis Jacq.) by chemical, physical, and enzymatic methods for bioethanol production.

Empty fruit bunch (EFB) of palm oil has significantly produced bioethanol by sequential delignification, saccharification (chemically and physically and afterwards enzymatic hydrolysis), and fermentation process. Mixing and soaking of EFB in 1% NaOH solution has reduced up to 90.3% lignin. Sequential pretreatment hydrolysis of EFB in sodium hydroxide solution and steaming in autoclave for 15 minutes, and subsequently the addition of xylanase and cellulase pH 6 and incubation for 6 days has shown the best process in which 19.34 – 20.56% sugars have been released. Scanning Electron Microscope (SEM) analysis has shown clearly visible alteration before and after hydrolysis. Pretreatment heating in microwave after acid or alkaline hydrolysis significantly damaged the cell structures. Fermentation process by Saccharomyces cerevisae was done in 2 days which 540 655 ml of 90% ethanol was resulted from pilot plant scale 3.82-4.63 kg EFB.
Achievable Rate Region under Linear Beamforming for Dual-Hop Multiple-Access Relay Network

Consider a network consisting of two independent single-antenna sources, a single-antenna destination and a helping multiple-antenna relay. This network is called a dual-hop multiple access relay network (MARN). In this network, sources transmit to the relay simultaneously in the first time slot. The relay retransmits the received sum-signal to the destination using a linear beamforming scheme in the second time slot. In this paper, we characterize the achievable rate region of MARN under linear beamforming. The achievable rate region characterization problem is first transformed to an equivalent “corner point” optimization problem with respect to linear beamforming matrix at the relay. Then, we present an efficient algorithm to solve it via only semi-definite programming (SDP). We further derive the mathematical close-forms of the maximum individual rates and the sum-rate. Finally, numerical results demonstrate the performance of the proposed schemes.
PLASMA SMELTING OF UNCONVENTIONAL ORES AND MINE RESIDUES FOR THE RECOVERY OF PRECIOUS METALS

Conventional mining operations can produce a wide range of wastes containing gold and other precious metals but which are less suitable for processing by traditional methods. There are also types of ore that do not fit easily into a traditional processing flowsheet, e.g. because the refractory nature of their minerology or the combination of valuable metals is less suitable for traditional processes. Nevertheless, these potential sources of valuable metals remain unexploited resources that have attracted growing interest in the metals and mining industries over recent years. The challenge is therefore to develop and use alternative processing methods that have the flexibility of operation to cope with a wide variety of material types and tonnages, whilst providing high recoveries for valuable metals. Tetronics’ DC plasma smelting technology achieves industry-leading recovery rates for precious metals from a wide range of input materials and initial precious metal concentrations, whilst generating an inert slag-based material as a by-product (even when starting from a hazardous waste input material), approved within the EU for use in construction applications. This paper describes how this technology can be used as a key part of an overall process for the recovery of precious metals from these interesting materials.
Detecting candidate cosmic bubble collisions with optimal filters

We review an optimal-filter-based algorithm for detecting candidate sources of unknown and differing size embedded in a stochastic background, and its application to detecting candidate cosmic bubble collision signatures in Wilkinson Microwave Anisotropy Probe (WMAP) 7-year observations. The algorithm provides an enhancement in sensitivity over previous methods by a factor of approximately two. Moreover, it is optimal in the sense that no other filter-based approach can provide a superior enhancement of these signatures. Applying this algorithm to WMAP 7-year observations, eight new candidate bubble collision signatures are detected for follow-up analysis.
Characterizing non-coding hits in genome-wide association studies using epigenetic data

Understanding the molecular basis of human disease is one of the greatest challenges of our time, and recent explosion in genetic and genomic datasets are finally putting it within reach. In the last ten years, genome-wide association studies have identified thousands of genetic variants associated with disease. However, the majority of these variants fall outside genes making interpreting their role in disease difficult. In parallel, the ENCODE and Roadmap Epigenomics consortia have produced high resolution annotations of the genome which identify large portions with potential regulatory function. We develop methods to interpret genome-wide association studies using these annotations to generate hypotheses about how associated variants contribute to disease mechanism. In particular, we go beyond the usual stringent p-value threshold to investigate variants with small individual effect sizes which current methods do not have power to detect. Evaluating our methods on the Wellcome Trust Case Control Consortium 7 Disease studies, we find associated variants are enriched in a variety of functional categories even after controlling for various biases. We also find an unprecedented number of variants contribute to this enrichment, supporting our hypothesis that the architecture of these diseases involves combinatorial interaction of many variants with small individual effect sizes. Thesis supervisor: Manolis Kellis Title: Associate Professor of Electrical Engineering and Computer Science
Multi-scale Clustering of Functional Data with Application to Hydraulic Gradients in Wetlands

A new set of methods are developed to perform cluster analy- sis of functions, motivated by a data set consisting of hydraulic gradients at several locations distributed across a wetland complex. The methods build on previous work on clustering of functions, such as Tarpey and Ki- nateder (2003) and Hitchcock et al. (2007), but explore functions generated from an additive model decomposition (Wood, 2006) of the original time se- ries. Our decomposition targets two aspects of the series, using an adaptive smoother for the trend and circular spline for the diurnal variation in the series. Dierent measures for comparing locations are discussed, including a method for eciently clustering time series that are of dierent lengths using a functional data approach. The complicated nature of these wetlands are highlighted by the shifting group memberships depending on which scale of variation and year of the study are considered.
0 Results and Discussion : 3 . 1 Isolation of Halophilic Bacteria and Screening for Lipolytic Enzyme Production :

Halophilic bacteria live in extreme salt conditions and can produce enzymes which are highly stable. A study was conducted to isolate lipolytic enzyme producing halophilic bacteria from salted fish. As lipolytic enzymes from halophiles can tolerate extreme salt conditions, it is highly stable and can tolerate harsh industrial conditions and thus has got potential biotechnological applications. In the present work 17 halophilic isolates were obtained from salted fish samples on Seghals Gibbons Complex (SGC) medium. The lipolytic activity of the strains was tested by Tween hydrolysis on SGC medium containing 1% Tween 80 or Tween 40. A strain SH1 which showed the highest relative enzyme activity was identified by 16 S rDNA sequencing as a novel strain belonging to the class Gamma proteobacteria. The strain was found to grow in a salt range, 2 to 5 M and thus confirmed as an extreme halophile. It showed lipolytic activity at salt concentration in the range 2 to 4 M and pH range 6 to 11. Thus it forms a promising strain for the production of halophilic lipolytic enzymes and thus can have industrial applications.
PT-CFI : Practical Backward-Edge Control Flow Integrity Using Intel Processor Trace

This paper presents PT-CFI, a new backward-edge control ﬂow integrity (CFI) based on a recently introduced hardware feature in Intel x86 called Processor Trace (PT). Designed primarily for of-ﬂine software debugging and performance analysis, PT offers the capability of tracing the entire control ﬂow of a running program. In this paper, we explore the practicality of using PT for security applications, and propose a new CFI model that enforces a perfect backward-edge policy for native COTS binaries based on the traces from Intel PT. By exploring the intrinsic tracing properties inside PT with a system call based synchronization primitive and a deep inspection capability, we have addressed a number of technical challenges such as how to make sure the backward edge CFI policy is both sound and complete, how to make PT enforce our CFI policy, and how to balance the performance overhead. We have implemented PT-CFI and evaluated with a number of programs including SPEC2006 and HTTP daemon. Our experimental results show that PT-CFI enforces strong backward-edge CFI with no false positive or false negative, as well as minor performance overhead.
Ripple phase formation in phosphatidylcholine: Effect of acyl chain relative length, position, and unsaturation

The ripple phases of a range of mixed-acid phosphatidylcholine derivatives with one saturated C18 chain and the other chain of variable length or unsaturation were studied using differential scanning calorimetry, x-ray diffraction, and freeze-fracture electron microscopy. The temperature dependences of their ripple wavelength ~l! , stacking repeat distance ( ds), and the monoclinic angle u m defining their unit cell were measured and found to be consistent with an inherently asymmetric ripple phase with an amplitude that slowly increases with temperature. The temperature spans of the ripple phases of the saturated mixed-chain derivatives and the derivatives containing unsaturated chains were found to be larger and smaller, respectively, than those of homoacyl derivatives of the same equivalent chain length. This was shown to be consistent with the slidingchain model proposed by Cevc ~Biochemistry 30, 7186‐7197, 1991! The tendency of phosphatidylcholine derivatives to form asymmetric ripple phases and the possible molecular organization of such phases are discussed in terms of different models. X-ray diffraction evidence was found for the existence of a secondary ripple phase with a wavelength about 1.8 times greater than the conventional pretransition ripple phase. This phase is formed in samples freshly cooled from the liquid-crystal phase and tends to persist longer than the conventional ripple phase on cooling to the gel phase. Freeze-fracture electron microscopy suggests that it is symmetric in cross section possibly reflecting the interaction of two opposing pretransition ripples. @S1063-651X~98! 12209-2#
PASSIVE LOCALIZATION OF PULSED SOUND SOURCES WITH A 2-HYDROPHONE ARRAY

An ultrasonic imaging system includes a beamformer stage operative to generate a frame of acoustic signals on an initial sample grid, and a display characterized by a pixel parameter. Acoustic signals on the initial sample grid are converted to processed acoustic signals on a modified sample grid prior to scan conversion. The modified sample grid is controlled as a function of the pixel parameter of the display to reduce image artifacts and maximize the detail with which the image is displayed.
Moving Object Trajectory Based Spatio-Temporal Mobility Prediction.

Mobility prediction for individual trajectory is a challenging topic. The aim of the study is to develop a simple but effective method to predict when the user will leave from the current location and where he will move next. The proposed method performs the predictions in three continuous, sequential phases. In the first phase, the method continuously extracts grid-based stay-time statistics from the GPS coordinate stream of the location-aware mobile device of the user. In the second phase, from the grid-based stay-time statistics, the method periodically extracts and manages regions that the user frequently visits. Finally, in the third phase, from the stream of region-visits, the method continuously estimates the parameters of an inhomogeneous continuous-time Markov model and in a continuous fashion predicts when the user will leave his current region and where he will move next. The temporal and spatial prediction accuracy of the method has been evaluated using a single long trajectory from the GeoLife data set. The results show that for the optimal method parameter settings, the method can predict the departure time on average to be within 83 minutes of the actual departure time and can predict the next region correctly in 51% of the cases.
The Influence of Context on Online Shopping Behavior: The Case of Concurrent TV Consumption

It is widely agreed that people use digital devices while watching TV, a phenomenon called "second Screening. " While previous research has shown that the diffusion of mobile devices can increase online sales, work on multitasking suggests that second screening may lead to lower sales. In this paper, we study the impact of concurrent TV viewership on online shopping behavior and try to understand the conflicting results. Our first study analyzes the impact of TV consumption on online shopping behavior at the aggregate level using a panel of 100,000 U.S. customers over a period of 24 months. We address potential endogeneity problems with an instrumental variable approach. Our second study proposes a novel approach to realizing individual-level analysis over thousands of consumers and products. We identify a sample of consumers who watched a certain TV show and analyze their Amazon shopping carts in the context of TV consumption. We then compare these results to the shopping behavior of the same sample of consumers one week before they watched the TV show. Both studies reveal that while TV consumption is correlated with a higher likelihood of online shopping in general, the causal effect of TV watching differs with respect to the complexity of the products purchased. If a TV program appeals to a large number of viewers, this results in fewer sales of high-complexity products (1% increase in TV consumption leads to -1% sales) and more sales of low-complexity products (1% increase in TV consumption leads to 6.5% sales). New insights on this effect and information about its magnitude could help marketers better allocate their TV ad spending and make better online shopping recommendations for online shoppers who concurrently consume TV.
Bayesian Deep Deconvolutional Neural Networks

We address the problem of sparse spike deconvolution from noisy measurements within a Bayesian paradigm that incorporates sparsity promoting priors about the ground truth. The optimization problem arising out of this formulation warrants an iterative solution, which we accomplish using a deep neural network (DNN). The architecture of the DNN is such that the weights and biases in each layer are fixed and determined by the blur matrix and the noisy measurements, and the sparsity promoting prior determines the activation function in each layer. In scenarios where the priors are not available exactly, but adequate training data is available, the formulation can be adapted to learn the priors by parameterizing the activation function using a linear expansion of threshold (LET) functions. As a proof of concept, we demonstrate successful spike deconvolution on synthetic dataset and compare our results with the fast iterative shrinkage-thresholding algorithm (FISTA). We also show an application of the proposed method for performing image reconstruction in super-resolution localization microscopy.
GrooveNet : Real-Time Music-Driven Dance Movement Generation using Artificial Neural Networks

We present the preliminary results of GrooveNet, a generative system that learns to synthesize dance movements for a given audio track in real-time. Our intended application for GrooveNet is a public interactive installation in which the audience can provide their own music to interact with an avatar. We investigate training artificial neural networks, in particular, Factored Conditional Restricted Boltzmann Machines (FCRBM) and Recurrent Neural Networks (RNN), on a small dataset of four synchronized music and motion capture recordings of dance movements that we have captured for this project. Our initial results show that we can train the FCRBM on this small dataset to generate dance movements. However, the model cannot generalize well to music tracks beyond the training data. We outline our plans to further develop GrooveNet.
Explicable Plans for Human-Robot Teams

For the robots to be part of our daily lives, their ability to form safe and successful collaborations with humans is a necessity. To ensure smoother collaborations, we provide a framework for human-robot teams using the concept of explicability. Explicability outlines that an agent’s perception of another agent’s model may be different from the actual model and that this should be factored into the agent’s planning process. Our contributions include extending explicability formulation to support interactive human-robot teaming and implementing the framework on a physical robotic platform. We make a reasonable assumption that an agent might only have an approximate version of other agent’s model, i.e. the robot not only has to learn human’s preconceptions about its own model, but also has to work with incomplete human planning preferences.
Vitamin D Deficiency in Elderly Ukrainian Population: Impact of Seasonal Factors

Background: Vitamin D deficiency (VDD) is one of the important factors contributing to the development of various chronic and often times debilitating diseases, such as multiple sclerosis, Alzheimer’s disease, atherosclerosis, neoplasms, etc. Yet to date only limited data about VDD in the elderly patients are available. Aim of the study: evaluation of the influence of seasonal factors, as well as subjects’ age, on the vitamin D status. Study population: 793 subjects aged 60-89 years (women 91.2%). Mean age was 68.7±6.6 years, and mean BMI was 28.3±5.0 kg/m 2 . The subjects were broken down into 3 age groups: 60-69, 70-79, and 80-89 years, with a subsequent assessment of vitamin D status by season of the year and by month. Results: During the winter, mean 25(OH) D levels in serum (31.3±19.4 nmol/l) were significantly lower compared to the summer ( 38.7±24.6 nmol/l), p<0.001. The lowest VDD frequency across the study population was found in August (45.5%) with severe VDD frequency being 6.8%. The VDD frequency was increasing from September onwards, with the highest value being recorded in February (96.6%), whereas for severe VDD the peak occurs in March with 59.4%. Conclusions: considering the frequency of VDD in the Ukrainian population aged between 60 and 89 years, we recommend the vitamin D supplements throughout the year, particularly during the end of the winter season when the vitamin D status is at its worst as compared to the rest of the year.
Applying the CDM model to firms in Mexico ’ s manufacturing industry : 2004

Following the Crépon-Duguet-Mairesse Model (1998), using a database with 2,078 Mexican manufacturing establishments from 2004 to 2006, adapting available proxy variables and proposing a different productivity estimation, this article sets out: Which is their innovation propensity? Which factors push on their innovation efforts? Are these efforts and the innovation favoring labor productivity? The main findings are: Mexican manufacturing establishments with a higher innovation propensity are the largest, with high technological intensity and market share. Advertising, knowledge appropriability, FDI, TT and access to credit have a positive effect on innovation efforts. Moreover, the innovation effort, together with export levels, FDI and access to technology have influenced the innovation of new processes and/or designs, particularly in local firms as compared to foreign firms. Finally, the variables: innovation, labour remunerations and capital intensity have a substantial effect on labor productivity and at a lower level market share, FDI and total quality control.