Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a DevOps Maturity Model: A validated model to evaluate the maturity of DevOps in organizations

As the approaches to implementing DevOps can be vastly different is has been hard to establish a guideline for the implementation of such an environment. At the same time, many organizations struggle with implementing the soft aspects of DevOps and to convince other parties in the organization to get on board as well. Yet, collaboration and cooperation are needed for a successful DevOps implementation, especially regarding management support. The goal of this study is to create a DevOps maturity model that will help in (1) guiding organizations to the required maturity level and in (2) demonstrating to other parties in the organization what is needed for a thriving DevOps environment. The DevOps maturity model is created based on a literature review and qualitative interviews with experts, followed by validation through expert opinions and put to practice in case studies. This study has both scientific and practical implications: Scientific: This study provides a new approach to DevOps maturity models and contributes to a limited amount of scientific research on DevOps maturity. Scientific: Empirical research in (South)East-Asia on DevOps, in general, is very limited. This study contributes and can be used as a comparison to other geographical areas. Practical: A common model, CMMI, has been adapted to fit a DevOps maturity assessment and should be able to be adapted easily inside an organization. Practical: The model can be used to improve the knowledge on DevOps throughout the organization, by showing what is necessary to achieve different maturity levels
Reinforcement learning vs human programming in tetherball robot games

Reinforcement learning of motor skills is an important challenge in order to endow robots with the ability to learn a wide range of skills and solve complex tasks. However, comparing reinforcement learning against human programming is not straightforward. In this paper, we create a motor learning framework consisting of state-of-the-art components in motor skill learning and compare it to a manually designed program on the task of robot tetherball. We use dynamical motor primitives for representing the robot's trajectories and relative entropy policy search to train the motor framework and improve its behavior by trial and error. These algorithmic components allow for high-quality skill learning while the experimental setup enables an accurate evaluation of our framework as robot players can compete against each other. In the complex game of robot tetherball, we show that our learning approach outperforms and wins a match against a high quality hand-crafted system.
Dynamic Nuclear Polarization and MRI for the study of cardiac metabolism

Dynamic Nuclear Polarization (DNP) is an emerging technique in magnetic resonance imaging (MRI) that is able to enhance or “hyperpolarize” the signal of 13 C by many orders of magnitude in a wide range of endogenous metabolites. This enables us to image not only an injected parent molecule such as pyruvate but also its metabolic fate due to enzymatic conversion. The technology is being used for kinetic studies in solutions of viable cells, ex-vivo in perfused organs, in-vivo in preclinical disease models and now with a successful phase I clinical trial there is the potential to translate these technologies into the clinic. DNP has great potential for the study of altered cardiac metabolism under pathophysiological conditions.
Unsupervised HMM posteriograms for language independent acoustic modeling in zero resource conditions

The task of language independent acoustic unit modeling in unlabeled raw speech (zero-resource setting) has gained significant interest over the recent years. The main challenge here is the extraction of acoustic representations that elicit good similarity between the same words or linguistic tokens spoken by different speakers and to derive these representations in a language independent manner. In this paper, we explore the use of Hidden Markov Model (HMM) based posteriograms for unsupervised acoustic unit modeling. The states of the HMM (which represent the language independent acoustic units) are initialized using a Gaussian mixture model (GMM) — Universal Background Model (UBM). The trained HMM is subsequently used to generate a temporally contiguous state alignment which are then modeled in a hybrid deep neural network (DNN) model. For the purpose of testing, we use the frame level HMM state posteriors obtained from the DNN as features for the ZeroSpeech challenge task. The minimal pair ABX error rate is measured for both the within and across speaker pairs. With several experiments on multiple languages in the ZeroSpeech corpus, we show that the proposed HMM based posterior features provides significant improvements over the baseline system using MFCC features (average relative improvements of 25% for within speaker pairs and 40% for across speaker pairs). Furthermore, the experiments where the target language is not seen training illustrate the proposed modeling approach is capable of learning global language independent representations.
A Review of EEG-Based Brain-Computer Interface Systems Design

A brain-computer interface (BCI) system can recognize the mental activities pattern by computer algorithms to control the external devices. Electroencephalogram (EEG) is one of the most common used approach for BCI due to the convenience and non-invasive implement. Therefore, more and more BCIs have been designed for the disabled people that suffer from stroke or spinal cord injury to help them for rehabilitation and life. We introduce the common BCI paradigms, the signal processing, and feature extraction methods. Then, we survey the different combined modes of hybrids BCIs and review the design of the synchronous/asynchronous BCIs. Finally, the shared control methods are discussed.
Is Single-View Fluoroscopy Sufficient in Guiding Cardiac Ablation Procedures?

The CARTO XP ablation system provides real-time data on 3D, color-coded maps of the electrical activity of the heart; however, it is expensive and can only use a dedicated costly magnetic catheter per patient intervention. The purpose of our study is to shorten the duration of the radiofrequency ablation procedure and increase its efficacy by developing an affordable prototype catheter navigation system that simulates the CARTO system. To obtain 3D geometrical data from catheter locations inside the heart chamber, we acquired only single-view images using an Integris Allura fluoroscope and estimated the depth of the mapping electrode using pattern recognition techniques. Validation was performed in ideal and clinical conditions. For phantom experiment, when using a 7-French catheter, the average recovered depth error was 2.05 ± 1.46 mm using a single image. However, when using the 8-French catheter, the average recovered depth error was 1.54 ± 1.29 mm. In clinical experimentation, the standard error of estimate for the estimated depth was about 13.1 mm and 10.1 mm, respectively, for the posterior and lateral views. In conclusion, this paper describes our achievements and shortfalls in developing an affordable fluoroscopic navigation system to guide RF catheter ablation of cardiac arrhythmias.
Using Weighted Oriented Optical Flow Histograms for Multimodal Speaker Diarization

Speaker diarization currently focuses on using audio features to partition an audio stream into speaker homogeneous speech regions, in other words to determine “who spoke when”. Recent speaker diarization corpora contains video recordings in addition to the commonly used audio. Thus, we investigated the benefits of incorporating video features, namely histograms of weighted oriented optical flow, into a state-of-the-art diarization sys tem. We found that although the video features did not perform well alone, after combining systems using audio and video features, we were able to improve the diarization error rate by 14% as compared to a speaker diarization system trained on audio-only features.
Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model

Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviors guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers) activate the representation of rewards (or “action-outcomes”, e.g., foods) while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods). The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a) the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b) three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c) the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and explains the results of several devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behavior.
Universal Reinforcement Learning Algorithms: Survey and Experiments

Many state-of-the-art reinforcement learning (RL) algorithms typically assume that the environment is an ergodic Markov Decision Process (MDP). In contrast, the field of universal reinforcement learning (URL) is concerned with algorithms that make as few assumptions as possible about the environment. The universal Bayesian agent AIXI and a family of related URL algorithms have been developed in this setting. While numerous theoretical optimality results have been proven for these agents, there has been no empirical investigation of their behavior to date. We present a short and accessible survey of these URL algorithms under a unified notation and framework, along with results of some experiments that qualitatively illustrate some properties of the resulting policies, and their relative performance on partially-observable gridworld environments. We also present an open-source reference implementation of the algorithms which we hope will facilitate further understanding of, and experimentation with, these ideas.
Analytical version control management in a hypertext system

In this paper it is shown how structural and cognitive versioning issues can be efficiently managed in a Petri nets based hypertextual model. The advantages of this formalism are enhanced by modular and structured modeling; modularity allows to focus the attention only on some modules, while giving the abstraction of the others. Each module owns metaknowledge that is useful in defining new layers and contexts. The central point of the data model is the formulation and resolution of three recurrence equations, effective in describing both the versioning and the derivation history; these equations permit to express in precise terms both the structural evolution (changes operated on specific nodes of the net) and the behavioral one (changes concerning browsing).
A Novel Graph-based Algorithm to Infer Recurrent Copy Number Variations in Cancer

Many cancers have been linked to copy number variations (CNVs) in the genomic DNA. Although there are existing methods to analyze CNVs from individual samples, cancer-causing genes are more frequently discovered in regions where CNVs are common among tumor samples, also known as recurrent CNVs. Integrating multiple samples and locating recurrent CNV regions remain a challenge, both computationally and conceptually. We propose a new graph-based algorithm for identifying recurrent CNVs using the maximal clique detection technique. The algorithm has an optimal solution, which means all maximal cliques can be identified, and guarantees that the identified CNV regions are the most frequent and that the minimal regions have been delineated among tumor samples. The algorithm has successfully been applied to analyze a large cohort of breast cancer samples and identified some breast cancer-associated genes and pathways.
Culture and Real-Time PCR Based Maternal Screening and Antibiotic Susceptibility for Group B Streptococcus: An Iranian Experience

Introduction: Vertical Transmission of group B streptococcus (GBS) from a vagina colonized mother to her infant upon rupture of membranes (ROM) or after the onset of labor can cause life-threatening infections in newborn. Although intrapartum antibiotic prophylaxis (IAP) can significantly decrease neonatal GBS diseases, this issue has potentiated the emergence of antibiotic resistance strains. Our study examined the colonization rate of GBS using real-time PCR and culture methods, and trends in antibiotic resistance of GBS isolates obtained from pregnant women in Khorramabad, Iran. Methods: In this cross-sectional study, two vaginal-rectal swabs were collected and analyzed separately from 100 pregnant women at 35-37 weeks of gestation by convenience sampling method. The specimens were subjected to GBS detection using real-time PCR assay and standard culture. Susceptibility pattern of the GBS isolates was examined using the disk diffusion method. Results: GBS carriage rate was 17% and 19% using culture and real-time PCR, respectively. In six samples, the culture was positive and the real-time PCR was negative. Sensitivity and specificity for real-time PCR were 72.7% and 96.1%, respectively using culture as the gold standard. Amongst twenty-two isolates examined, 100% resistance to erythromycin and clindamycin was observed. One isolate (4%) exhibited resistance to penicillin. Conclusion: Considering the relatively high GBS carriage rate in Khorramabad, routine antepartum screening for GBS is recommended. Penicillin can remain the antibiotic of choice for IAP; however, in penicillin-allergic mothers, vancomycin can be an alternative antibiotic.
Elevated urine uranium excretion by soldiers with retained uranium shrapnel.

The use of depleted uranium in munitions has given rise to a new exposure route for this chemically and radioactively hazardous metal. A cohort of U.S. soldiers wounded while on or in vehicles struck by depleted uranium penetrators during the Persian Gulf War was identified. Thirty-three members of this cohort were clinically evaluated, with particular attention to renal abnormalities, approximately 3 y after their injury. The presence of retained shrapnel was identified by x ray, and urine uranium concentrations were measured on two occasions. The absorption of uranium from embedded shrapnel was strongly suggested by measurements of urine uranium excretion at two time intervals: one in 1993/1994 and one in 1995. Mean urine uranium excretion was significantly higher in soldiers with retained shrapnel compared to those without shrapnel at both time points (4.47 vs. 0.03 microg g(-1) creatinine in 1993/1994 and 6.40 vs. 0.01 microg g(-1) creatinine in 1995, respectively). Urine uranium concentrations measured in 1995 were consistent with those measured in 1994/1993, with a correlation coefficient of 0.9. Spot urine measurements of uranium excretion were also well correlated with 24-h urine collections (r = 0.95), indicating that spot urine samples can be reliably used to monitor depleted uranium excretion in the surveillance program for this cohort of soldiers. The presence of uranium in the urine can be used to determine the rate at which embedded depleted uranium fragments are releasing biologically active uranium ions. No evidence of a relationship between urine uranium excretion and renal function could be demonstrated. Evaluation of this cohort continues.
A new method for soccer shot detection with multi-resolution DCT

Shot is often used as a basic unit for video analysis and indexing. A shot may be defined as a sequence of frames captured by ”a single camera in a single continuous action in time and space”. The extraction of this unit (Shot) still present problems for sports video. In this paper, we propose a novel method for soccer shot detection using discrete cosine transform multi-resolution (DCT-MR). First, we detect the dominant color component by supposing that playfield pixels are green (dominant color). Second, we compute the discreet cosine transform with different resolution (from the lowest resolution to the highest). Finally, we use a prefixed threshold to distinguish different transitions among shots.
Sparse Representation of a Blur Kernel for Blind Image Restoration

Blind image restoration is a non-convex problem which involves restoration of images from an unknown blur kernel. The factors affecting the performance of this restoration are how much prior information about an image and a blur kernel are provided and what algorithm is used to perform the restoration task. Prior information on images is often employed to restore the sharpness of the edges of an image. By contrast, no consensus is still present regarding what prior information to use in restoring from a blur kernel due to complex image blurring processes. In this paper, we propose modelling of a blur kernel as a sparse linear combinations of basic 2-D patterns. Our approach has a competitive edge over the existing blur kernel modelling methods because our method has the flexibility to customize the dictionary design, which makes it well-adaptive to a variety of applications. As a demonstration, we construct a dictionary formed by basic patterns derived from the Kronecker product of Gaussian sequences. We also compare our results with those derived by other state-of-the-art methods, in terms of peak signal to noise ratio (PSNR).
Water-Membrane Partition Thermodynamics of an Amphiphilic Lipopeptide: An Enthalpy-Driven Hydrophobic Effect

To shed light on the driving force for the hydrophobic effect that partitions amphiphilic lipoproteins between water and membrane, we carried out an atomically detailed thermodynamic analysis of a triply lipid modified H-ras heptapeptide anchor (ANCH) in water and in a DMPC (1,2-dimyristoyl-sn-glycero-3-phosphocholine) bilayer. Combining molecular mechanical and continuum solvent approaches with an improved technique for solute entropy calculation, we obtained an overall transfer free energy of ∼−13 kcal mol−1. This value is in qualitative agreement with free energy changes derived from a potential of mean force calculation and indirect experimental observations. Changes in free energies of solvation and ANCH conformational reorganization are unfavorable, whereas ANCH-DMPC interactions—especially van der Waals—favor insertion. These results are consistent with an enthalpy-driven hydrophobic effect, in accord with earlier calorimetric data on the membrane partition of other amphiphiles. Furthermore, structural and entropic analysis of molecular dynamics-generated ensembles suggests that conformational selection may play a hitherto unappreciated role in membrane insertion of lipid-modified peptides and proteins.
Compact Self-Diplexing Dual-Band Dual-Sense Circularly Polarized Array Antenna With Closely Spaced Operating Frequencies

A novel self-diplexing aperture-shared dual-band dual-sense circularly polarized (CP) antenna array is proposed for transmitting/receiving (Tx/Rx) applications in X-band satellite communication systems. The antenna is capable of receiving the left-handed CP (LHCP) signals in the low band while transmitting the right-handed CP (RHCP) signals in the high band. First, a dual-port cavity-fed linearly polarized (LP) patch antenna with high isolation is developed as the element of the array. The element is composed of a slotted patch, a U-slot loaded substrate integrated cavity (SIC), and two groups of split-ring resonators (SRRs). The LHCP and RHCP operations are achieved by employing two sequentially rotated feeding networks which are isolated by a ground plane. To reduce the mutual coupling between the radiators, narrow slots are etched out in the center of the patches. Importantly, the sharing of patches at different bands (i.e., common aperture) enables a compact footprint to be achieved for the array, which is beneficial for suppressing undesirable grating lobes due to the reduced interelement spacing. The measured results of a fabricated prototype agree well with the simulations, showing an impedance bandwidth of 7.2–7.75 GHz (7.85–8.4 GHz), 3 dB axial ratio (AR) bandwidth of 7.4–7.7 GHz (7.95–8.32 GHz), and gain of 14 dBic (14.2 dBic) at the low band (high band). The channel isolation is over 27 dB and the sidelobe levels are below −15 dB in both bands. The demonstrated low-profile dual-band, dual-sense CP antenna array can be a good candidate for satellite communication.
On the taxonomic position of the enigmatic genus Tonkinodentus Schileyko, 1992 (Chilopoda, Scolopendromorpha): the first molecular data

Abstract The taxonomic position of the monotypic Vietnamese genus Tonkinodentus Schileyko, 1992 (for T.lestes Schileyko, 1992) has been considered in the light of the first obtained molecular data. Both molecular (28S rRNA) and morphological data support the position of this extraordinary eye-less genus within the family Scolopendridae Leach, 1814, a sighted clade, and thus suggests the polyphyly of blind scolopendromorphs. The species diagnosis has been amended and color images of T.lestes provided for the first time.
Quantitative Quality Assessment of Microscopic Image Mosaicing

The mosaicing technique has been employed in more and more application fields, from entertainment to scientific ones. In the latter case, often the final evaluation is still left to human beings, that assess visually the quality of the mosaic. Many times, a lack of objective measurements in microscopic mosaicing may prevent the mosaic from being used as a starting image for further analysis. In this work we analyze three different metrics and indexes, in the domain of signal analysis, image analysis and visual quality, to measure the quality of different aspects of the mosaicing procedure, such as registration errors and visual quality. As the case study we consider the mosaicing algorithm we developed. The experiments have been carried out by considering mosaics with very different features: histological samples, that are made of detailed and contrasted images, and live stem cells, that show a very low contrast and low detail levels.
Towards real time 3D tracking and reconstruction on a GPU using Monte Carlo simulations

This paper addresses the problem of camera tracking and 3D reconstruction from image sequences, i.e., the monocular SLAM problem. Traditionally, this problem is solved using non-linear minimization techniques that are very accurate but hardly used in real time. This work presents a highly parallelizable random sampling approach based on Monte Carlo simulations that fits very well on the graphics hardware. The proposed algorithm achieves the same precision as non linear optimization, getting real time performance running on commodity graphics hardware. Both accuracy and performance are evaluated using synthetic data and real video sequences captured with a hand-held camera. Moreover, results are compared with an implementation of Bundle Adjustment showing that the presented method gets similar results in much less time.