Visual requirements analytics: a framework and case study

For many software projects, keeping requirements on track needs an effective and efficient path from data to decision. Visual analytics creates such a path that enables the human to extract insights by interacting with the relevant information. While various requirements visualization techniques exist, few have produced end-to-end value to practitioners. In this paper, we advance the literature on visual requirements analytics by characterizing its key components and relationships in a framework. We follow the goal–question–metric paradigm to define the framework by teasing out five conceptual goals (user, data, model, visualization, and knowledge), their specific operationalizations, and their interconnections. The framework allows us to not only assess existing approaches, but also create tool enhancements in a principled manner. We evaluate our enhanced tool support through a case study where massive, heterogeneous, and dynamic requirements are processed, visualized, and analyzed. Working together with practitioners on a contemporary software project within its real-life context leads to the main finding that visual analytics can help tackle both open-ended visual exploration tasks and well-structured visual exploitation tasks in requirements engineering. In addition, the study helps the practitioners to reach actionable decisions in a wide range of areas relating to their project, ranging from theme and outlier identification, over requirements tracing, to risk assessment. Overall, our work illuminates how the data-to-decision analytical capabilities could be improved by the increased interactivity of requirements visualization.     

A Precise Statistical approach for concept change detection in unlabeled data streams

Recently data stream has been extensively explored due to its emergence in a great deal of applications such as sensor networks, web click streams and network flows. One of the most important challenges in data streams is concept change where data underlying distributions change from time to time. A vast majority of researches in the context of data stream mining are devoted to labeled data, whereas, in real word human practice label of data are rarely available to the learning algorithms. Moreover, most of the methods that detect changes in unlabeled data stream merely deal with numerical data sets, and also, they are facing considerable difficulty when dimension of data tends to increase. In this paper, we present a Precise Statistical approach for Concept Change Detection in unlabeled data streams, which, abbreviated as PSCCD, detects changes using an exchangeable test. This hypothesis test is driven from a martingale which is based on Doob’s Maximal Inequality. The advantages of our approach are three fold. First, it does not require a sliding window on the data stream whose size is a well-known challenging issue; second, it works well in multi-dimensional data stream, and last but not the least, it is applicable to different types of data including categorical, numerical and mixed-attribute data streams. To explore the advantages of our approach, quite a lot of experiments with different settings and specifications are conducted. The obtained results are very promising.     

Chemical functionalization of graphene oxide by a hindered amine stabilizer and evaluation of the product as a UV-stabilizer for polypropylene

The present work was devoted to synthesis of a novel UV-stabilizer based on graphene oxide for polypropylene. Hence, we grafted a hindered amine compound on graphene oxide through an amidation reaction. Fourier transform infrared (FTIR) spectroscopy and thermo-gravimetric analysis confirmed the grafting reaction. Also, X-ray diffraction together with scanning electron microscopy showed that the grafting reaction transforms the compact layered structure of graphene oxide into finer aggregation of loosely connected particles. Polypropylene film samples containing either of graphene oxide and the functionalized graphene oxide were prepared by solution mixing/anti-solvent precipitation followed by compression molding. Transmission electron microscopy showed significantly better dispersion of the modified graphene oxide in the polymer matrix compared to the pristine graphene oxide. Afterwards, the film samples were exposed to an artificial sunlight at 25?°C. It was proved by FTIR spectroscopy and tensile test that the modified graphene oxide at a concentration of 0.5?wt. % can efficiently retard the rate of the photo-oxidation of PP.     

Performance Assessment of the Linear,Nonlinear and Nonparametric Data Driven Models in River Flow Forecasting

In recent years, the data-driven modeling techniques have gained more attention in hydrology and water resources studies. River runoff estimation and forecasting are one of the research fields that these techniques have several applications in them. In the current study, four common data-driven modeling techniques including multiple linear regression, K-nearest neighbors, artificial neural networks and adaptive neuro-fuzzy inference systems have been used to form runoff forecasting models and then their results have been evaluated. Also, effects of using of some different scenarios for selecting predictor variables have been studied. It is evident from the results that using flow data of one or two month ago in the predictor variables dataset can improve accuracy of results. In addition, comparison of general performances of the modeling techniques shows superiority of results of KNN models among the studied models. Among selected models of the different techniques, the selected KNN model presented best performance with a linear correlation coefficient equal to 0.84 between observed flow data and predicted values and a RMSE equal to 2.64.     

Morphological region-based initial contour algorithm for level set methods in image segmentation

Multimedia Tools and Applications - Initial Contour (IC) is the essential step in level set image segmentation methods due to start the efficient process. However, the main issue with IC is how to...     

Aggregation-Enhanced Photoluminescence and Photoacoustics of Atomically Precise Gold Nanoclusters in Lipid Nanodiscs (NANO2)

The authors designed a structurally stable nano-in-nano (NANO²) system highly capable of bioimaging via an aggregation-enhanced NIR excited emission and photoacoustic response achieved based on atomically precise gold nanoclusters protected by linear thiolated ligands [Au₂₅(SC_nH_2n+1)₁₈, n  = 4–16] encapsulated in discoidal phospholipid bicelles through a one-pot synthesis. The detailed morphological characterization of NANO² is conducted using cryogenic transmission electron microscopy, small/wide angle X-ray scattering with the support of molecular dynamics simulations, providing information on the location of Au nanoclusters in NANO². The photoluminescence observed for NANO² is 20–60 times more intense than that of the free Au nanoclusters, with both excitation and emission wavelengths in the near-infrared range, and the photoacoustic signal is more than tripled. The authors attribute this newly discovered aggregation-enhanced photoluminescence and photoacoustic signals to the restriction of intramolecular motion of the clusters’ ligands. With the advantages of biocompatibility and high cellular uptake, NANO² is potentially applicable for both in vitro and in vivo imaging, as the authors demonstrate with NIR excited emission from in vitro A549 human lung and the KB human cervical cancer cells.     

Stabilisation of water in water Pickering emulsion containing gelatin and maltodextrin by bitter vetch protein nanoparticles

The incompatibility between water soluble polymers is one of the interactions between the different constituents of the food systems, which eventually leads to system instability due to phase separation. The use of natural nanoparticles under the Pickering mechanism is a promising technique for the stabilisation of water-in-water (W/W) emulsions. In this study, bitter vetch protein nanoparticles (BPNs), which was produced by ultrasonic treatment, was used to stabilise W/W emulsion. Then additional tests were performed to investigate the structure and properties of the nanoparticles and stabilised emulsion. Scanning electron microscopy (SEM) images showed the size of BPNs to be about 60 nm. The zeta potential of protein nanoparticle solution was about 50 mV. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD) and simultaneous thermal analysis (STA) confirmed that chemical structure and thermal behaviour of BPNs did not change during nanoparticle preparation process. BPNs could stabilise W/W emulsion containing gelatin and maltodextrin. Confocal laser scanning microscopy (CLSM) images confirmed the stability of gelatin/maltodextrin emulsion containing BPNs. The drop test confirmed the presence of maltodextrin in the continuous phase of the emulsion. The stabilised W/W emulsion had 100% stability against stress and temperature of 80 °C. Rheology analysis showed the viscoelastic nature and pseudoplastic behaviour of the stabilised emulsion.