Miracle-dz/UniFAR-FTUs huggingface dataset:
'Play your own way': adapting a procedural framework for accessibility

Sure Footing is a research project cum commercial product that arose from exploring generative systems for infinite runner games that balanced procedural flexibility with authorial intent. The approach taken as part of the games level generation systems allows not only for the gradual increase in difficulty as players progress, but allow for specific configurations that allow for the unique configurations that embody a level of difficulty or challenge. This paper explores ongoing development of the generation framework to to enable this flexibility, as well as raise consideration for flexible generation in the context of accessibility for players of varying competencies.
Compressed Sensing Parallel MRI with Adaptive Shrinkage TV Regularization

Compressed sensing (CS) methods in magnetic resonance imaging (MRI) offer rapid acquisition and improved image quality but require iterative reconstruction schemes with regularization to enforce sparsity. Regardless of the difficulty in obtaining a fast numerical solution, the total variation (TV) regularization is a preferred choice due to its edge-preserving and structure recovery capabilities. While many approaches have been proposed to overcome the non-differentiability of the TV cost term, an iterative shrinkage based formulation allows recovering an image through recursive application of linear filtering and soft thresholding. However, providing an optimal setting for the regularization parameter is critical due to its direct impact on the rate of convergence as well as steady state error. In this paper, a regularizer adaptively varying in the derivative space is proposed, that follows the generalized discrepancy principle (GDP). The implementation proceeds by adaptively reducing the discrepancy level expressed as the absolute difference between TV norms of the consistency error and the sparse approximation error. A criterion based on the absolute difference between TV norms of consistency and sparse approximation errors is used to update the threshold. Application of the adaptive shrinkage TV regularizer to CS recovery of parallel MRI (pMRI) and temporal gradient adaptation in dynamic MRI are shown to result in improved image quality with accelerated convergence. In addition, the adaptive TV-based iterative shrinkage (ATVIS) provides a significant speed advantage over the fast iterative shrinkage-thresholding algorithm (FISTA).
Differentially Private Verification of Regression Predictions from Synthetic Data

One approach for releasing public use files is to make synthetic data, i.e., data simulated from statistical models estimated on the confidential data. Given access only to synthetic data, users cannot tell whether the synthetic data have been constructed in ways that provide sufficient accuracy for their particular purposes. To enable users to make such assessments, data providers also can allow users to request verification measures. These are summary statistics reflecting comparisons of the results of analysis based on the synthetic and confidential data. We present three verification measures that satisfy differential privacy for assessing the quality of linear regression models. We use simulation studies to illustrate the verification measures.
Variation in abundance of predicted resistance genes in the Brassica oleracea pangenome

Summary Brassica oleracea is an important agricultural species encompassing many vegetable crops including cabbage, cauliflower, broccoli and kale; however, it can be susceptible to a variety of fungal diseases such as clubroot, blackleg, leaf spot and downy mildew. Resistance to these diseases is meditated by specific disease resistance genes analogs (RGAs) which are differently distributed across B. oleracea lines. The sequenced reference cultivar does not contain all B. oleracea genes due to gene presence/absence variation between individuals, which makes it necessary to search for RGA candidates in the B. oleracea pangenome. Here we present a comparative analysis of RGA candidates in the pangenome of B. oleracea. We show that the presence of RGA candidates differs between lines and suggests that in B. oleracea, SNPs and presence/absence variation drive RGA diversity using separate mechanisms. We identified 59 RGA candidates linked to Sclerotinia, clubroot, and Fusarium wilt resistance QTL, and these findings have implications for crop breeding in B. oleracea, which may also be applicable in other crops species.
Filter-Aided Sample Preparation Procedure for Mass Spectrometric Analysis of Plant Histones

Characterization of histone post-translational modifications (PTMs) is still challenging, and robust histone sample preparation is essential for convincing evaluation of PTMs by mass spectrometry. An effective protocol for extracting plant histone proteins must also avoid excessive co-extraction of the numerous potential interfering compounds, including those related to secondary metabolism. Currently, the co-existence of histone marks is addressed mostly by shotgun proteomic analysis following chemical derivatization of histone lysine residues. Here, we report a straightforward approach for plant histone sample preparation for mass spectrometry, based on filter-aided sample preparation coupled with histone propionylation. The approach offers savings in sample handling and preparation time, enables removal of interfering compounds from the sample, and does not require either precipitation or dialysis of histone extract. We show the comparison of two protocol variants for derivatization of histone proteins, in-solution propionylation in the vial and propionylation on the filter unit. For both protocols, we obtained identical abundances of post-translationally modified histone peptides. Although shorter time is required for histone protein labeling on the filter unit, in-solution derivatization slightly outweighed filter-based variant by lower data variability. Nevertheless, both protocol variants appear to be efficient and convenient approach for preparation of plant histones for mass spectrometric analysis.
Test Framework for Jenkins Shared Libraries

Delivering new features to users as fast and efficiently as possible is a common challenge on software development. A continuous delivery and integration pipeline can be used for this purpose, since it provides an automated way to orchestrate a series of steps for building, versioning, testing and deploying an application code. The newest version of Jenkins works with a concept called pipeline-as-code, where instead of configuring jobs on Jenkins to build, deploy or test the application, the user can implement a shared library with the same purpose. As a result, Jenkins job settings have been transformed into Groovy code, which enables the development of automated unit tests. However, using this pipeline brings the challenge of guaranteeing the high quality of this orchestration process. Unit tests by themselves are not enough to make sure that the implementation of new pipeline features did not add new bugs or changed the pipeline behavior. To do this validation we need to write functional tests, but that requires understanding of Jenkins and Groovy which can make this task complex. For this reason, a test framework was created as a shared library to facilitate the creation of functional tests for Jenkins shared libraries. The framework can execute several pipeline scenarios described by the user and apply them to different projects. The framework helps to ensure a high quality in the pipeline code used to ship your application from development environment until production, resulting in a more reliable process.
Occurrence, Antibiotic Resistance, and Population Diversity of Listeria monocytogenes Isolated From Fresh Aquatic Products in China

Listeria monocytogenes is an important Gram-positive foodborne pathogen. However, limited information is available on the comprehensive investigation and potential risk of L. monocytogenes in fresh aquatic products, which are popular to consumers in China. This study aimed to determine the occurrence, virulence profiles, and population diversity of L. monocytogenes isolated from aquatic products in China. In total, 846 aquatic product samples were collected between July 2011 and April 2016 from 43 cities in China. Approximately 7.92% (67/846) aquatic product samples were positive for L. monocytogenes, 86.57% positive samples ranged from 0.3 to 10 MPN/g, whereas 5.97% showed over 110 MPN/g by the Most Probable Number method, which included two samples of products intended to be eaten raw. Serogroups I.1 (serotype 1/2a), I.2 (serotype 1/2b), and III (serotype 4c) were the predominant serogroups isolated, whereas serogroup II.1 (serotype 4b) was detected at much lower frequencies. Examination of antibacterial resistance showed that nine antibacterial resistance profiles were exhibited in 72 isolates, a high level susceptibility of 16 tested antibiotics against L. monocytogenes were observed, indicating these common antibacterial agents are still effective for treating L. monocytogenes infection. Multilocus sequence typing revealed that ST299, ST87, and ST8 are predominant in aquatic products, indicating that the rare ST299 (serotype 4c) may have a special ecological niche in aquatic products and associated environments. Except llsX and ptsA, the 72 isolates harbor nine virulence genes (prfA, actA, hly, plcA, plcB, iap, mpl, inlA, and inlB), premature stop codons (PMSCs) in inlA were found in four isolates, three of which belonged to ST9. A novel PMSC was found in 2929-1LM with a nonsense mutation at position 1605 (TGG→TGA). All ST87 isolates harbored the ptsA gene, whereas 8 isolates (11.11%) carried the llsX gene, and mainly belonged to ST1, ST3, ST308, ST323, ST330, and ST619. Taken together, these results first reported potential virulent L. monocytogenes isolates (ST8 and ST87) were predominant in aquatic products which may have implications for public health in China. It is thus necessary to perform continuous surveillance for L. monocytogenes in aquatic products in China.
The First International Workshop on Web Data Processing & Reasoning (WDPAR 2018)

The first Web Data Processing & Reasoning (WDPAR) workshop in conjunction with the 41st German Conference on Artificial Intelligence (KI) taking place in Berlin, Germany in 2018 is a forum for all researchers especially interested in processing of and reasoning on web data. The proceedings of WDPAR@KI 2018 are published in the Open Journal of Web Technologies (OJWT) (www.ronpub.com/ojwt) as special issue and the publisher of OJWT is RonPub. This editorial provides an overview over the aims and scope of the workshop and the review procedure. Furthermore, we introduce the accepted papers and their topics in the editorial.
MultiSoft

We introduce MultiSoft, a multilayer soft sensor capable of sensing real-time contact localization, classification of deformation types, and estimation of deformation magnitudes. We propose a multimodal sensing pipeline that carries out both inverse problem solving and machine learning tasks. Specifically, we employ an electrical impedance tomography (EIT) for contact localization and a support vector machine (SVM) for classifying deformations and regressing their magnitudes. We propose a deformation-aware system which enables maintaining a persistent single-point contact localization throughout the deformation. By updating a time-varying distribution of conductivity change caused by deformations, a single-point contact localization can be maintained and restored to support interaction using both contact localization and deformations.We devise a multilayer structure to fabricate a highly stretchable and flexible soft sensor with a short sensor settlement after excitations. Through a series of experiments and evaluations, we validate both raw sensor and multimodal sensing performance with the proposed method. We further demonstrate applicability and feasibility of MultiSoft with example applications.
User Information Augmented Semantic Frame Parsing using Coarse-to-Fine Neural Networks

Semantic frame parsing is a crucial component in spoken language understanding (SLU) to build spoken dialog systems. It has two main tasks: intent detection and slot filling. Although state-of-the-art approaches showed good results, they require large annotated training data and long training time. In this paper, we aim to alleviate these drawbacks for semantic frame parsing by utilizing the ubiquitous user information. We design a novel coarse-to-fine deep neural network model to incorporate prior knowledge of user information intermediately to better and quickly train a semantic frame parser. Due to the lack of benchmark dataset with real user information, we synthesize the simplest type of user information (location and time) on ATIS benchmark data. The results show that our approach leverages such simple user information to outperform state-of-the-art approaches by 0.25% for intent detection and 0.31% for slot filling using standard training data. When using smaller training data, the performance improvement on intent detection and slot filling reaches up to 1.35% and 1.20% respectively. We also show that our approach can achieve similar performance as state-of-the-art approaches by using less than 80% annotated training data. Moreover, the training time to achieve the similar performance is also reduced by over 60%.
The Role of Persulfide Metabolism During Arabidopsis Seed Development Under Light and Dark Conditions

The sulfur dioxygenase ETHE1 oxidizes persulfides in the mitochondrial matrix and is involved in the degradation of L-cysteine and hydrogen sulfide. ETHE1 has an essential but as yet undefined function in early embryo development of Arabidopsis thaliana. In leaves, ETHE1 is strongly induced by extended darkness and participates in the use of amino acids as alternative respiratory substrates during carbohydrate starvation. Thus, we tested the effect of darkness on seed development in an ETHE1 deficient mutant in comparison to the wild type. Since ETHE1 knock-out is embryo lethal, the knock-down line ethe1-1 with about 1% residual sulfur dioxygenase activity was used for this study. We performed phenotypic analysis, metabolite profiling and comparative proteomics in order to investigate the general effect of extended darkness on seed metabolism and further define the specific function of the mitochondrial sulfur dioxygenase ETHE1 in seeds. Shading of the siliques had no morphological effect on embryogenesis in wild type plants. However, the developmental delay that was already visible in ethe1-1 seeds under control conditions was further enhanced in the darkness. Dark conditions strongly affected seed quality parameters of both wild type and mutant plants. The effect of ETHE1 knock-down on amino acid profiles was clearly different from that found in leaves indicating that in seeds persulfide oxidation interacts with alanine and glycine rather than branched-chain amino acid metabolism. Sulfur dioxygenase deficiency led to defects in endosperm development possibly due to alterations in the cellularization process. In addition, we provide evidence for a potential role of persulfide metabolism in abscisic acid (ABA) signal transduction in seeds. We conclude that the knock-down of ETHE1 causes metabolic re-arrangements in seeds that differ from those in leaves. Putative mechanisms that cause the aberrant endosperm and embryo development are discussed.
Mortality after admission for heart failure in the UK compared with Japan

Objective Mortality amongst patients hospitalised for heart failure (HHF) in Western and Asian countries may differ, but this has not been investigated using individual patient-level data (IPLD). We sought to remedy this through rigorous statistical analysis of HHF registries and variable selection from a systematic literature review. Methods and results IPLD from registries of HHF in Japan (n=3781) and the UK (n=894) were obtained. A systematic literature review identified 23 models for predicting outcome of HHF. Five variables appearing in 10 or more reports were strongly related to prognosis (systolic blood pressure, serum sodium concentration, age, blood urea nitrogen and creatinine). To compare mortality in the UK and Japan, variables were imputed in a propensity model using inverse probability of treatment weighting (IPTW) and IPTW with logistic regression (doubly robust IPTW). Overall, patients in the UK were sicker and in-patient and post-discharge mortalities were greater, suggesting that the threshold for hospital admission was higher. Covariate-adjusted in-hospital mortality was similar in the UK and Japan (IPTW OR: 1.14, 95% CI 0.70 to 1.86), but 180-day postdischarge mortality was substantially higher in the UK (doubly robust IPTW OR: 2.33, 95% CI 1.58 to 3.43). Conclusions Despite robust methods to adjust for differences in patient characteristics and disease severity, HHF patients in the UK have roughly twice the mortality at 180 days compared with those in Japan. Similar analyses should be done using other data sets and in other countries to determine the consistency of these findings and identify factors that might inform healthcare policy and improve outcomes.
Texture Segmentation of Urinary Sediment Image based on a Weighted Gaussian Mixture Model with Markov Random Fields

In clinical laboratory studies, it is of importance for urinary sediment images because the composition and quantity of specific cells contained in the images revealing information for the diagnosis of urinary and renal diseases in humans. In this paper, we propose a method for the segmentation of urine sediment image using the magnification of 20-fold microscopy based on Markov model. This method selects sum average feature derived from the spatial gray co-occurrence matrix for the classification in the neighborhood window with the size of 7×7. First, each pixel in the image is evaluated by its intensity dispersion in comparison with the average intensity of the 7×7 neighborhood, and the pixels with higher dispersion would be further proceeded by Gaussian mixture model. Here the weights are designed feature distance matrix within the window to obtain similar, so as to re-estimate the features. Edge positioning is more accurate and clear compared with the traditional morphological methods by using the method of texture segmentation about urinary sediment image in this paper which can make it avoids cell adhesion caused by too closer proximity of cells and there is no glitch in the target area.
Automatic Benchmark Generation Framework for Malware Detection

To address emerging security threats, various malware detection methods have been proposed every year. Therefore, a small but representative set of malware samples are usually needed for detection model, especially for machine-learning-based malware detection models. However, current manual selection of representative samples from large unknown file collection is labor intensive and not scalable. In this paper, we firstly propose a framework that can automatically generate a small data set for malware detection. With this framework, we extract behavior features from a large initial data set and then use a hierarchical clustering technique to identify different types of malware. An improved genetic algorithm based on roulette wheel sampling is implemented to generate final test data set. The final data set is only one-eighteenth the volume of the initial data set, and evaluations show that the data set selected by the proposed framework is much smaller than the original one but does not lose nearly any semantics.
Towards an enterprise architecture controlling framework

Enterprise Architecture defines the methods by which information technology use can be aligned to the organizational goals through the definition of standards and controlling the alignment to said standards. We propose an extension of EA methods so that they can be applied to controlling the evolution of software-intensive systems across the organization. Our extension takes the form of a framework: a metamodel for defining compliance models and views that can be constructed using said compliance models. We document how and where in the architecture management process these compliance models are constructed and how they can be leveraged. We demonstrate through a 2-year case study that the information obtained in the views described in our framework is accurate and efficient when compared to the guidance in enterprise architecture frameworks such as TOGAF [16] and Managed Evolution [10].
Self-transformative effects of designing videogames and the challenge of capturing them quantitatively: a case study

In this work we discuss the self-transformative effects that can be elicited through the practice of videogame design. We focus on the problems and limitations that we encountered in trying to quantitatively capture psychological changes in videogame designers. Our work involved two experiments, each of them observing a group of M.Sc. students in a digital games course working on small, serious videogame projects. In these experiments, we attempted to identify and track transformations that the students themselves underwent while trying to stimulate psychological changes in their players by means of gameplay. The results of our case study suggest that the research setup we designed could not rigorously measure the quantitative kinds of self-transformation we were after. Even so, our work takes some important, initial steps in the direction of understanding and exploring the practice of videogame design as offering novel possibilities and advantages in the contexts of self-transformation.
Interactive CNC simulation using distance fields

ABSTRACT Real-time simulation of numerical control machining is important to the virtual manufacturing process. It is used to optimise the product design and production plan on a virtual environment, thus saving on the overall manufacturing cost and time. A real-time geometric simulation is desirable because the faster is the simulation, the greater is its capabilities to detect errors. Real-time interactive simulation allows faster iterations in the design phase. However, a precise and real-time interactive simulation is a difficult problem because it requires an efficient representation for complex dynamic volumes while offering interactive real-time rendering of the scene. This research proposes a novel solution for precise and real-time interactive CNC (Computer Numerical Control) simulation using signed distance fields (SDFs) with a data structure for dynamic narrow-band level sets called VDB. The real-time interactive rendering is then solved with a ray-casting algorithm. Tests were performed and SDF was concluded to provide efficient Boolean operations and memory usage, and real-time interactive rendering of its models was possible using ray casting.
Adjunct Proceedings of the 10th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications

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