Recognizing and understanding user behaviors from screencasts

User interacts with computers or mobile devices, leading to user behaviors on screen. In the context of software engineering, analyzing user behavior enables many applications such as intelligent bug fix, code completion and knowledge recommendation for developers. Such technique can be extended to more general knowledge worker environment, in which users have to manipulate devices according to specific guidelines. Existing works rely heavily on software instrumentation to obtain user actions from operation systems, which is hard to deploy and maintain. In addition, considering the security and privacy of some scenarios, non-intrusive is the major requirement to be included in the system. In this work, we leverage Computer Vision and Natural Language Processing techniques to recognize and understand user behaviors from screencasts, which is a non-intrusive and cross-platform method. We first recognize 10 categories of low level user actions such as mouse moving and type text, then summarize them to higher level abstractions (i.e. line-granularity coding steps). We also try to interpret user interaction with applications by multi-task learning and generate structured language descriptions (i.e. command, widget and location). Finally, unsupervised learning method is introduced for GUI linting problem, which is taken as a case study of user behavior analysis. To train the deep neural networks, we collect diverse video data from YouTube, Twitch and Bugzilla, and manually label them to build the dataset. The experiment results demonstrate the high performance of proposed method, and the user study validate the practical applications of many downstream tasks

Distinguishing Look-Alike Innocent and Vulnerable Code by Subtle Semantic Representation Learning and Explanation

Though many deep learning (DL)-based vulnerability detection approaches have been proposed and indeed achieved remarkable performance, they still have limitations in the generalization as well as the practical usage. More precisely, existing DL-based approaches (1) perform negatively on prediction tasks among functions that are lexically similar but have contrary semantics; (2) provide no intuitive developer-oriented explanations to the detected results. In this paper, we propose a novel approach named SVulD, a function-level Subtle semantic embedding for Vulnerability Detection along with intuitive explanations, to alleviate the above limitations. Specifically, SVulD firstly trains a model to learn distinguishing semantic representations of functions regardless of their lexical similarity. Then, for the detected vulnerable functions, SVulD provides natural language explanations (e.g., root cause) of results to help developers intuitively understand the vulnerabilities. To evaluate the effectiveness of SVulD, we conduct large-scale experiments on a widely used practical vulnerability dataset and compare it with four state-of-the-art (SOTA) approaches by considering five performance measures. The experimental results indicate that SVulD outperforms all SOTAs with a substantial improvement (i.e., 23.5%-68.0% in terms of F1-score, 15.9%-134.8% in terms of PR-AUC and 7.4%-64.4% in terms of Accuracy). Besides, we conduct a user-case study to evaluate the usefulness of SVulD for developers on understanding the vulnerable code and the participants' feedback demonstrates that SVulD is helpful for development practice.Comment: Accepted By FSE'2

Non-equilibrium behaviour in coacervate-based protocells under electric-field-induced excitation

Although numerous strategies are now available to generate rudimentary forms of synthetic cell-like entities, minimal progress has been made in the sustained excitation of artificial protocells under non-equilibrium conditions. Here we demonstrate that the electric field energization of coacervate microdroplets comprising polylysine and short single strands of DNA generates membrane-free protocells with complex, dynamical behaviours. By confining the droplets within a microfluidic channel and applying a range of electric field strengths, we produce protocells that exhibit repetitive cycles of vacuolarization, dynamical fluctuations in size and shape, chaotic growth and fusion, spontaneous ejection and sequestration of matter, directional capture of solute molecules, and pulsed enhancement of enzyme cascade reactions. Our results highlight new opportunities for the study of non-equilibrium phenomena in synthetic protocells, provide a strategy for inducing complex behaviour in electrostatically assembled soft matter microsystems and illustrate how dynamical properties can be activated and sustained in microcompartmentalized media.SCI(E)[email protected]; [email protected]

Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation

Due to convenience, open-source software is widely used. For beneficial reasons, open-source maintainers often fix the vulnerabilities silently, exposing their users unaware of the updates to threats. Previous works all focus on black-box binary detection of the silent dependency alerts that suffer from high false-positive rates. Open-source software users need to analyze and explain AI prediction themselves. Explainable AI becomes remarkable as a complementary of black-box AI models, providing details in various forms to explain AI decisions. Noticing there is still no technique that can discover silent dependency alert on time, in this work, we propose a framework using an encoder-decoder model with a binary detector to provide explainable silent dependency alert prediction. Our model generates 4 types of vulnerability key aspects including vulnerability type, root cause, attack vector, and impact to enhance the trustworthiness and users' acceptance to alert prediction. By experiments with several models and inputs, we confirm CodeBERT with both commit messages and code changes achieves the best results. Our user study shows that explainable alert predictions can help users find silent dependency alert more easily than black-box predictions. To the best of our knowledge, this is the first research work on the application of Explainable AI in silent dependency alert prediction, which opens the door of the related domains

Choroidal thickness and vascular microstructure parameters in Chinese school-age children with high hyperopia using optical coherence tomography

BackgroundThe current study was to evaluate the choroidal thickness (CT) and vascular microstructure parameters in Chinese children with high hyperopia through enhanced depth imaging optical coherence tomography (EDI-OCT).MethodsCross-sectional study. A total of 23 children with high hyperopia and 29 children with normal refractive status were retrospectively enrolled in the study. The measurement of the macular CT, 7 points: the sub-foveal area point, the temporal and nasal points at a radius of 0.5-mm, 1.5-mm, and 3-mm were measured. After binarization of the OCT images, the total choroidal area (TCA), stromal area (SA) as well as the luminal area (LA) were identified and measured. The choroidal vascularity index (CVI) was defined as the ratio of LA to TCA. The independent t-test for normal distributions and Kruskal-Wallis tests for non-normal distributions were used to compare other parameters between groups. The Tamhane's T2 test was performed to adjust for multiple comparisons between groups within each analysis.ResultsThe subfoveal CT (SFCT) in the high hypermetropic group was significantly thicker than that in normal controls (309.22 ± 53.14 μm vs. 291.27 ± 38.27 μm; P = 0.019). At 0.5 mm, 1.5 mm, and 3.0 mm in diameter, the nasal choroidal sectors of the high hyperopia eyes were significantly thicker than that of the control (P < 0.05). There was significant difference in the choroidal vascular parameters. TCA and LA in the high hyperopia eyes was significantly larger than that of the normal control eyes (3078129.54 ± 448271.18 μm2 vs. 2765218.17 ± 317827.19 μm2, 1926819.54 ± 229817.56 μm2 vs. 1748817.18 ± 191827.98 μm2; P = 0.009, P = 0.011; Table 2). SA values were 1086287.55 ± 212712.11 um2 in the high hyperopia eyes and 999712.71 ± 209838.12 μm2 in the control eyes. The CVI and LA/SA ratio values were differed significantly in the two groups (P = 0.019, P = 0.030, respectively). AL was significantly correlated with SFCT (r = −0.325, P = 0.047), but not significantly correlated with other parameters. Spherical equivalent (SE) was significantly correlated with AL and SFCT (r = −0.711, r = 0.311; P = 0.001, P = 0.016), whereas no significant association between sphere and other parameters.ConclusionThe choroidal structure of the high hyperopia eyes was different from the normal control eyes. The thicker SFCT, higher LA, and TCA were characteristic of high hyperopia eyes. Choroidal blood flow may be decreased in amblyopic eyes. SFCT of high hyperopia children abnormally increased and correlated with shorter AL and higher SE. AL and SE affect choroidal structure and vascular density

Considering neighborhood effects improves individual dbh growth models for natural mixed-species forests in Mexico

International audienceAbstractKey messageMore accurate diameter at breast height (dbh)-growth models are needed for developing management tools for mixed-species forests in Mexico. Individual distance-dependent dbh growth models that quantify local neighborhood effects have been developed for four species groups in such forests. The performance of the models is improved by distinguishing between inter- and intraspecific group competitions.ContextThe management of mixed-species forests in the northwest of Durango, Mexico, is mainly based on the selection method. Understanding the interspecific and intraspecific competition is critical to developing management tools for such mixed-species forests.AimsAn individual-based distance-dependent modeling approach was used to model the growth of dbh and to evaluate neighborhood effects for four species groups in Mexican mixed-species stands.MethodsTwenty-two species were classified into four groups: Pinus (seven species), other conifers (three species), other broadleaves (four species), and Quercus (eight species). Four methods were used to select neighboring trees and 12 competition indices (CIs) were calculated. Comparisons of the neighboring trees selection methods and CIs and tests of assumptions about neighborhood effects were conducted.ResultsIntra-species-group competition significantly reduced diameter growth for all species groups, except for the Quercus group. The Pinus, other conifers, and Quercus groups had significant and negative neighborhood effects on the other broadleaves species group, and not vice versa. The Quercus group also had negative neighborhood effect on the Pinus and other conifers species groups, and not vice versa. The Pinus and other conifers species groups had negative neighborhood effects on each other. All fitted age-independent dbh growth models showed a good of fit to the data (adjusted coefficient of determination larger than 0.977).ConclusionThe growth models can be used to predict dbh growth for species groups and competition in mixed-species stand from Durango, Mexico

Chloctanspirones A and B, novel chlorinated polyketides with an unprecedented skeleton, from marine sediment derived fungus Penicillium terrestre

K

Local and General Above-Ground Biomass Functions for Pinus palustris Trees

There is an increasing interest in estimating biomass for longleaf pine (Pinus palustris Mill.), an important tree species in the southeastern U.S. Most of the individual-tree allometric models available for the species are local, relying on stem diameter outside bark at breast height (DBH) and total tree height (HT), but seldom include stand-level variables such as stand age, basal area or stand density. Using the biomass dataset of 296 longleaf pine trees sampled in the southeastern U.S. by different forestry research institutions, we developed a set of local and general systems of tree biomass equations to predict total tree total above-stump biomass, bole biomass outside bark, live branch biomass and live foliage biomass. The local systems were based on DBH or DBH and HT, and the general systems included in addition to DBH and HT, stand-level variables such as age, basal area and stand density. This paper reports the first set of general allometric equations reported for longleaf pine trees. These systems of biomass equations provide tools to support managers in making management decisions for the species in a variety of ecological, silvicultural and economics applications. The systems can be applied to trees growing over a large geographical area and having a wide range of ages and stand characteristics

Studies of Ionic Current Rectification Using Polyethyleneimines Coated Glass Nanopipettes

The modification of glass nanopipettes with polyethyleneimines (PEIs) has been successfully achieved by a relatively simple method, and the smallest tip opening is around 3 nm. Thus, in a much wider range of glass pipettes with radii from several nanometers to a few micrometers, the ion current rectification (ICR) phenomenon has been observed. The influences of different KCl concentrations, pH values, and tip radii on the ICR are investigated in detail. The sizes of PEIs have been determined by dynamic light scattering, and the effect of the sizes of PEIs for the modification, especially for a few nanometer-pipettes in radii, is also discussed. These findings systemically confirm and complement the theoretical model(7,18) and provide a platform for possible selectively molecular detection and mimic biological ion channels