Miracle-dz/UniFAR-FTUs huggingface dataset:
Value creation and capture: a model of the software development process

Landmark Graphics supplies software and services to the upstream oil and gas industry. Our software portfolio, which ranges from exploration and drilling to data management and decision analysis, includes more than 60 products consisting of over 50 million lines of source code. For many years, Landmark has been collecting project metrics we wished to harvest to gain insight into key business questions in three areas: optimal release cycle duration (scope/time trade-off), optimal project staffing levels, effects of uncertainty. We set out to develop a relatively simple project dynamics model to use in conjunction with market sensitivity and economic analysis to help optimize profitability. Some of our ideas and results are similar to those of Preston Smith and Donald Reinertsen, who examined the impact of time-to-market sensitivity. However, our approach is a more detailed model tuned to software development issues.
Measuring engagement in a classroom: Synchronised neural recordings during a video presentation

The efficacy of learning in a classroom depends on how engaged students are with the learning material. Is it possible to assess students' engagement, not with questionnaires or surveys, but directly from their brain activity without distracting them from the material at hand? We demonstrate that classroom engagement can be measured using students' neural responses to a naturalistic stimulus. Many aspects of attentional modulation, which were previously demonstrated in a laboratory setting are reliably reproduced here in a classroom with portable low-cost electroencephalography (EEG) recorded simultaneously from multiple students. The present data suggests that evoked neural responses to video stimulus, known to be modulated by attention, can be used to assess the engagement of a group of students in real-time.
Matrix string theory on pp-waves

After a brief review on matrix string theory on flat backgrounds, we formulate matrix string models on different pp-wave backgrounds. This will be done both in the cases of constant and variable RR background flux for certain exact string geometries. We exhibit the non-perturbative representation of string interaction and show how the eigenvalue tunnelling drives the WKB expansion to give the usual perturbative string interaction also in supersymmetric pp-wave background cases.
OPTIMAL POWER FLOW IN RECTANGULAR FORM VIA AN INTERIOR POINT METHOD

In this paper we describe an I terior Point Method (IPM) to solve large scaleNonLinear Programming (NLP) problems, tailored to the solution of a specialized Optimal Power Plow (OPF) formulation that uses bus voltages in rectangular coordinates. The distinctive feature of thi s OPF formulation is that the objective function and constrain s are quadratic functions, and such quadratic properties are explored in the development of a robust nonlinear OPF solution procedure for application in heavily loaded power systems.
Micropatterning Co-cultures of Epithelial Cells on Filter Insert Substrates

A key feature of mature epithelium is the presence of an apical-basal polarization. In vitro this is achieved by culturing either pure populations of epithelial cells or unorganized mixtures of primary cell populations harvested from epithelial tissues in a filter insert culture system. In addition to epithelial cells however, multiple other cell types are also present in normal epithelium in a specific organization. Generally the organization of component cells within a tissue is critical for normal function. The importance of the organization of different cell populations within normal and diseased epithelium in vitro has received little attention, in part due to the lack of available methods for spatially controlling multiple cell populations while maintaining the culture conditions necessary to generate polarized and mature epithelium. We have developed a set of micropatterning techniques to spatially control the organization of co-cultures of epithelial cells on filter insert membranes under the culture conditions necessary to induce epithelial cell polarization. We describe a fibronectin microprinting method that allows arbitrary pattern generation and a parafilm insert method that allows only simple stripe geometry patterning but does not require the photolithographic equipment that the fibronectin microprinting method requires, and can be adopted by any epithelial biology lab. We also demonstrate that our micropatterning methods do not alter the formation of cilia, a marker of epithelium maturation. Our methods provide a novel tool for studying epithelial biology in polarized epithelial sheets containing multiple cell populations with controlled organization.
homeBLOX: introducing process-driven home automation

Home automation promises more convenience for residential living. We propose process-driven home automation as an approach to reduce the difficulty of specifying automation tasks without restricting users in terms of customizability and complexity of supported scenarios. Our graph-based user interface abstracts from the complexity of process specification, while created sequences are automatically translated into BPEL code for execution. Our homeBLOX architecture extends a process engine with the capabilities to communicate with heterogeneous smart devices, integrate virtual devices, and support different home automation protocols. We report on initial user tests with our automation interface and demonstrate the customizability and expressiveness of our system based on realized example use cases.
A Neural Network Algorithm with Weight Decay for 2-6 Hour Ahead Wind Speed Forecasting

A single layer feed forward neural network algorithm using back propagation, gradient descent and weight decay is proposed for the purpose of wind speed forecasting using only the observed hourly wind speeds, directions, temperatures and pressures observed at at a single site. The site data used for this experiment was 10 years worth of hourly ASOS data from the Bismarck North Dakota Regional Airport gathered from the NOAA ftp site which is available for users upon subscription. The experiment was coded in the R programming language using the package implementation nnet which implements a single layer feed forward neural network with an optional weight decay factor.
Using visual strategies to support verbal comprehension in an adolescent with Down syndrome

It has been frequently reported that children with Down syndrome have deficits in verbal short-term memory while having relatively good performance in visual short-term memory tasks. Such verbal deficits have a detrimental effect on various high-level cognitive processes, most notably language comprehension. In this study, we report the case of an adolescent with Down syndrome whose verbal short-term memory and comprehension capacities are impaired. Noting that his visual memory remained relatively well preserved, we developed a remediation strategy based on his visual abilities to support his verbal memory deficit. This remediation led to significant improvements in verbal short-term memories of story and thematic units as well as in overall story comprehension. These results are consistent with recent findings suggesting that deficits in verbal short-term memory can be compensated by efficient visual memory abilities.
Cross-lingual transfer learning during supervised training in low resource scenarios

In this study, transfer learning techniques are presented for cross-lingual speech recognition to mitigate the effects of limited availability of data in a target language using data from richly resourced source languages. First, a maximum likelihood (ML) based regularization criterion is used to learn context-dependent Gaussian mixture model (GMM) based hidden Markov model (HMM) parameters for phones in target language using data from both target and source languages. Recognition results indicate improved HMM state alignments. Second, the hidden layers of a deep neural network (DNN) are initialized using unsupervised pre-training of a multilingual deep belief network (DBN). The DNN is fine-tuned jointly using a modified cross entropy criterion that uses HMM state alignments from both target and source languages. Third, another DNN fine-tuning technique is explored where the training is performed in a sequential manner source language followed by the target language. Experiments conducted using varying amounts of target data indicate further improvements in performance can be obtained using joint and sequential training of the DNN compared to existing techniques. Turkish and English were chosen to be the target and source languages respectively.
Feed-forward trajectory specification for flapping-wing flying robot

A trajectory specification method for a flapping-wing flying robot exhibiting a novel dynamical model than other types of micro aerial vehicles such as quadrotors and airplanes is presented. The desired trajectory is first defined as a vector-valued, differentiable parametric curve of the form f: Rα → R>. The velocity vector at any point of the trajectory is the same as the unit tangent vector obtained using parametric differentiation. The acquired unit tangent vector is subsequently expressed in quaternion form and nominal values for the pitch and yaw angles at every point of the trajectory are determined. Finally, the feed-forward trajectory is specified as a function of the input control variables (δr, δe) of the flapping wing flying robot. At the same time, the feasibility of a specified trajectory is verified so that there are no violations of intrinsic constraints. Both two and three dimensional flight trajectories are simulated for exemplification. Simulation results are compared to real sensor data, thus verifying the procedure.
The Use of Two Authentication Factors to Enhance the Security of Mass Storage Devices

Recently, removable (external USB) mass storage devices are finding widespread utilization for transferring and backing up data. They even can be used as bootable devices. They have brought us great convenience, yet, simultaneously, they are very vulnerable to the extraction of private and confidential data from computer systems. Some access authentication schemes have been proposed for removable storage media as means of ensuring the security of the data. For the most part, such schemes use an Internet connection and a main server as a trusted third party, which generally provides an inadequate level of security. In this paper, we analyze the security risks of these schemes and propose a novel approach that takes into account both the users and the physical properties of the media so as to join the user and removable storage media. We involve users' smart-phones in the authentication process as a second authentication factor. Then, only legitimate users can authenticate themselves with their smart-phones to obtain access to their removable storage media. An analysis of the security associated with this scheme is presented. Our mechanism will leverage the general public to secure their credentials through their USB storage devices, without the need of an Internet connection or communication with a trusted third party.
Farming systems ecology : towards ecological intensification of world agriculture

The model of intensification that is deeply rooted in the mind of scientists from the green revolution generation is obsolete. We need to think outside the box. Agriculture needs knowledge-intensive management systems to improve food security and incomes in the South, and to reduce the dependence on external (fossil fuel) inputs in the North. The design of landscapes that support an ecologically intensive agriculture creates opportunities for synergies between food production and ecosystem services. Most importantly, this can contribute to detoxify or food and the environment.
A Marker Locating Method Based on Gray Centroid Algorithm and its Application to Displacement and Strain Measurement

A new type of marker locating method based on the gray centroid algorithm is developed. The new algorithm takes higher resolution and less computational complexity. A verification experiment is completed and the results are presented that a 2% error can be obtained if the resolution large than 0.05 pixel. Two application examples of this new algorithm of marker locating method are described in this paper.
A Bayesian Perfusion estimation using spatio-temporal priors in ASL-MRI

Arterial Spin Labeling (ASL) is a non-invasive technique for generating perfusion images of the brain. Since it does not require the use of an artificial tracer, it has been significantly gaining recognition as a method for obtaining perfusion parameters. Following an alternating labeling/control acquisition sequence, the minor magnetization difference between labeled and non-labeled images is detected by performing image subtraction. Due to the intrinsically low Signal to Noise Ratio (SNR), label/control acquisitions must be repeated a number of times and signal averaging is then performed in order to detect the small perfusion dependent difference signal. In this work, a novel ASL data analysis approach is proposed, in which the averaging procedure is not done explicitly. The magnetization difference estimation problem is formulated in a Bayesian framework, where spatio-temporal priors are used to deal with the ill-posed nature of the estimation task, hence enabling shorter acquisition times, without compromising the estimation quality. The a priori assumption that no drastic signal variations are expected along the same tissue, except at the organs boundaries, is imposed by the priors. To evaluate the performance of the proposed algorithm, the results obtained using synthetic data were compared against the two most common subtraction methods used to process ASL data, pair-wise and surround subtraction. The results were very encouraging, as greater SNR was achieved and the overall mean-squared error was highly reduced. The proposed algorithm was then applied to a real data-set, also obtaining satisfactory results.
Image composition with color harmonization

Image composition is a very important technique in computer generated imagery (CGI). Besides some factors such as contrast, texture, noise that affect the quality of the composition, color harmony between fore- and background is also an important issue that should be carefully dealt with. However, in the previous image composition techniques, the color harmony between fore- and background is seldom considered. In this paper, an optimization method is presented to deal with the color harmonization problem that used in image composition. A cost function is derived from the observation of the local smoothness of the hue values. And the image is harmonized by minimizing the cost function. A new matching function is presented to select the best matching harmonic schemes, and a new component based pre-harmonization strategy is also presented to preserve the hue distribution of the harmonized images. Our approach overcomes several shortcomings of the existed color harmonization methods. We validate the performance of the method and demonstrate its effectiveness in a variety of experiments.
Insulin Resistance Alters Islet Morphology in Nondiabetic Humans

Type 2 diabetes is characterized by poor glucose uptake in metabolic tissues and manifests when insulin secretion fails to cope with worsening insulin resistance. In addition to its effects on skeletal muscle, liver, and adipose tissue metabolism, it is evident that insulin resistance also affects pancreatic β-cells. To directly examine the alterations that occur in islet morphology as part of an adaptive mechanism to insulin resistance, we evaluated pancreas samples obtained during pancreatoduodenectomy from nondiabetic subjects who were insulin-resistant or insulin-sensitive. We also compared insulin sensitivity, insulin secretion, and incretin levels between the two groups. We report an increased islet size and an elevated number of β- and α-cells that resulted in an altered β-cell–to–α-cell area in the insulin- resistant group. Our data in this series of studies suggest that neogenesis from duct cells and transdifferentiation of α-cells are potential contributors to the β-cell compensatory response to insulin resistance in the absence of overt diabetes.
Energy Efficient Real-Time Scheduling in Distributed Systems

Battery powered real-time systems have been widely used in many applications. As the quantity and the functional complexity of battery powered devices continue to increase, energy efficient design of such devices has become important. Also these realtime systems have to concurrently perform a multitude of complex tasks with strict time constraints. Thus, minimizing power consumption and extending battery life while guaranteeing the timing constraints has become a critical aspect in designing such systems. Moreover, energy consumption is also a critical issue in parallel and distributed systems. We present novel algorithms for energy efficient scheduling of Directed Acyclic Graph (DAG) based applications on Dynamic Voltage Scaling (DVS) enabled systems. Experimental results show that our proposed algorithms give better results than the existing algorithms.
Predicting Peptide Structures in Native Proteins from Physical Simulations of Fragments

It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations.
Interactive Autonomous Baby Sitting Robot

This paper describes a baby sitting robot system, which has the capabilities of baby tracking , cry signal processing, map building and obstacle avoidance, path planning, entertain the baby or alarm the guardian if any defined anomalies found and voice interaction. For that purpose, the baby sitting robot system has a capability to track and follow the baby with obstacle avoidance. It is also potential enough to identify when the baby cries, or reach near to any defined anomalies. Further more it provides a convenient voice interface for easy usage and various entertainment services for the baby. Rather than just notify the parents that the baby is approaching anomalies, it will try to prevent it from reaching the anomalies by either blocking or distracting with music. In this paper, the overall system is introduced and the detailed subsystems and functions are presented. Baby tracking, cry detection and step detection have been implemented and provide successful results. The navigation planner which includes obstacle detection and path planning and voice interaction has been also implemented successfully. Integration of all these subsystems has been archived with processing delay.
Audio forensics from gunshot for firearm identification

The gunshot blast is one of the evidences used to identify the criminal gun in the shooting scene. We proposed a method of using the gunshot blast to identify the firearm caliber in terms of audio forensics. The experiments were performed on the database consisting of 80 audio files of gunshot sounds fired from 8 different guns. At first, the autocorrelation functions of the gunshot sounds were calculated and normalized in the preprocessing stage. Then the Fourier cosine coefficients could be used to reduce the fluctuation and to extract the features of gunshot sounds. In addition, the gun sounds could also be verified by evaluating the difference among the Fourier cosine coefficients of the reference, and then the gun caliber could be identified.
Characterization of the Nonlinear Thermal Resistance and Pulsed Thermal Dynamic Behavior of AlGaN–GaN HEMTs on SiC

A laboratory setup, along with a set of measurement and identification procedures, have been developed expressly for the characterization of the thermal behavior of AlGaN/GaN HEMTs, suitable for microwave high power amplifier (HPA) design. The setup allows the measurement of the drain current time-domain dynamic response to positive drain bias pulses, performed at different temperatures and different dissipated power densities. The proposed measurement conditions discriminate thermal phenomena from electrical dispersive effects for this particular technology. Both the thermal resistance and the “transient thermal resistance” are identified for a single-cell 1-mm device and for a 4-mm power-bar composed of four devices, designed to be used as the final stage of a monolithic $C$ -band HPA for pulsed radar application. Transient data allow to compute the device operative channel temperature as a function of the pulsewidth and duty cycle, which is a crucial feature for pulsed HPA applications, typical for the GaN technology. The measured thermal data point out the nonlinearity of the thermal resistance versus dissipated power and base-plate temperature and the consequent critical thermal issue inherent in physically packing together such devices.
DynRBLA: A High-Performance and Energy-Efficient Row Buffer Locality-Aware Caching Policy for Hybrid Memories

Phase change memory (PCM) is a promising memory technology that can offer higher memory capacity than DRAM. Unfortunately, PCM’s access latencies and energies are higher than DRAM and its endurance is lower. DRAM-PCM hybrid memory systems use DRAM as a cache to PCM, to achieve the low access latencies and energies, and high endurance of DRAM, while taking advantage of the large PCM capacity. A key question is what data to cache in DRAM to best exploit the advantages of each technology while avoiding their disadvantages as much as possible. We propose DynRBLA, a fundamentally new caching policy that improves hybrid memory performance and energy efficiency. Our observation is that both DRAM and PCM contain row buffers which cache the most recently accessed row. Row buffer hits incur the same latency in DRAM and PCM, whereas row buffer misses incur longer latencies in PCM. To exploit this, we devise a policy which tracks the access and row buffer misses of a subset of recently used rows in PCM, and caches in DRAM only rows which are likely to miss in the row buffer and be reused. Compared to a cache management technique that only takes into account the frequency of accesses to data, our row buffer locality-aware scheme improves performance by 15% and energy efficiency by 10%. DynRBLA improves system performance by 17% over an all-PCM memory, and comes to within 21% of the performance of an unlimitedsize all-DRAM memory system.