Miracle-dz/UniFAR-FTUs huggingface dataset:
Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware

Windows malware detectors based on machine learning are vulnerable to adversarial examples, even if the attacker is only given black-box query access to the model. The main drawback of these attacks is that: ( $i$ ) they are query-inefficient, as they rely on iteratively applying random transformations to the input malware; and ( $ii$ ) they may also require executing the adversarial malware in a sandbox at each iteration of the optimization process, to ensure that its intrusive functionality is preserved. In this paper, we overcome these issues by presenting a novel family of black-box attacks that are both query-efficient and functionality-preserving, as they rely on the injection of benign content (which will never be executed) either at the end of the malicious file, or within some newly-created sections. Our attacks are formalized as a constrained minimization problem which also enables optimizing the trade-off between the probability of evading detection and the size of the injected payload. We empirically investigate this trade-off on two popular static Windows malware detectors, and show that our black-box attacks can bypass them with only few queries and small payloads, even when they only return the predicted labels. We also evaluate whether our attacks transfer to other commercial antivirus solutions, and surprisingly find that they can evade, on average, more than 12 commercial antivirus engines. We conclude by discussing the limitations of our approach, and its possible future extensions to target malware classifiers based on dynamic analysis.
The Correlation of Outflow Kinematics with Star Formation Rate. VI. Gas Outflows in AGNs

We investigate the connection between ionized gas outflows and star formation activity using a large sample of type 1 and 2 active galactic nuclei (AGNs) with far-infrared detections or star formation rate (SFR) estimates. The strength of ionized gas outflows, measured by the velocity dispersion and velocity shift of the [O iii] emission line, clearly shows a correlation with SFR. The connection between specific star formation rate (sSFR) and [O iii] gas velocity dispersion indicates that AGNs with stronger outflows are hosted by galaxies with higher SFR. Compared with star-forming galaxies in the main sequence, both type 1 and type 2 AGNs show sSFRs similar to that of non-AGN galaxies, indicating no instantaneous AGN feedback, while sSFR is higher (lower) for AGNs with stronger (weaker) outflows than that of main-sequence galaxies. These results are consistent with a delayed AGN feedback scenario. However, it is also possible that a decease/increase of gas fraction may cause the correlation without AGN feedback.
Separating common and idiosyncratic components without moment condition and on the weighted $L_1$ minimization path.

In large-dimensional factor analysis, existing methods, such as principal component analysis (PCA), assumed finite fourth moment of the idiosyncratic components, in order to derive the convergence rates of the estimated factor loadings and scores. However, in many areas, such as finance and macroeconomics, many variables are heavy-tailed. In this case, PCA-based estimators and their variations are not theoretically underpinned. In this paper, we investigate into the weighted $L_1$ minimization on the factor loadings and scores, which amounts to assuming a temporal and cross-sectional quantile structure for panel observations instead of the mean pattern in $L_2$ minimization. Without any moment constraint on the idiosyncratic errors, we correctly identify the common and idiosyncratic components for each variable. We obtained the convergence rates of a computationally feasible weighted $L_1$ minimization estimators via iteratively alternating the quantile regression cross-sectionally and serially. Bahardur representations for the estimated factor loadings and scores are provided under some mild conditions. In addition, a robust method is proposed to estimate the number of factors consistently. Simulation experiments checked the validity of the theory. Our analysis on a financial data set shows the superiority of the proposed method over other state-of-the-art methods.
Regularised orthogonal machine learning for nonlinear semiparametric models

This paper proposes a Lasso-type estimator for a high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools. We first adjust the moment function so that the gradient of the future loss function is insensitive (formally, Neyman orthogonal) with respect to the first-stage regularisation bias, preserving the single index property. We then take the loss function to be an indefinite integral of the adjusted moment function with respect to the single index. The proposed Lasso estimator converges at the oracle rate, where the oracle knows the nuisance function and solves only the parametric problem. We demonstrate our method by estimating the short-term heterogeneous impact of Connecticut’s Jobs First welfare reform experiment on women’s welfare participation decision.
Coastal dune modelling from airborne LiDAR, terrestrial LiDAR and Structure from Motion–Multi View Stereo

In this work, we present an analysis of the Garopaba dune field, southern Brazil, based on data from Airborne LiDAR (ALS surveyed in 2010), Terrestrial LiDAR (TLS surveyed in 2019) and Structure from Motion–Multi View Stereo (SfM-MVS surveyed in 2019). Although sand dunes are commonly regarded as a challenge to traditional photogrammetry due their homogeneous texture and spectral response, in this research image matching was successful in all areas of the survey due the presence of superficial features (footprints and sandboard tracks) and visibility of the sedimentary stratification, highlighted by heavy minerals. The SfM-MVS DEM accuracy was evaluated by comparison with a TLS DEM, resulting in RMSE of 0.08m and MAE of 0.06m. Displacement of dune crest lines from the ALS and SfM-MVS DEMs resulted in a migration rate of ≈5 m/year between 2010 and 2019, in good agreement with rates derived from satellite images and historical aerial photographs of the same area. SfM-MVS is a low-cost solution with fast and reliable results for 3D modelling and continuous monitoring of coastal dunes.
Reward Value Revealed by Auction in Rhesus Monkeys

Economic choice is thought to involve the elicitation of the subjective values of the choice options. Thus far, value estimation in animals has relied on stochastic choices between multiple options presented in repeated trials and expressed from averages of dozens of trials. However, subjective reward valuations are made moment-to-moment and do not always require alternative options; their consequences are usually felt immediately. Here, we describe a Becker–DeGroot–Marschak (BDM) auction-like mechanism that provides more direct and simple valuations with immediate consequences. The BDM encourages agents to truthfully reveal their true subjective value in individual choices (“incentive compatibility”). Male monkeys reliably placed well-ranked BDM bids for up to five juice volumes while paying from a water budget. The bids closely approximated the average subjective values estimated with conventional binary choices (BCs), thus demonstrating procedural invariance and aligning with the wealth of knowledge acquired with these less direct estimation methods. The feasibility of BDM bidding in monkeys paves the way for an analysis of subjective neuronal value signals in single trials rather than from averages; the feasibility also bridges the gap to the increasingly used BDM method in human neuroeconomics. SIGNIFICANCE STATEMENT The subjective economic value of rewards cannot be measured directly but must be inferred from observable behavior. Until now, the estimation method in animals was rather complex and required comparison between several choice options during repeated choices; thus, such methods did not respect the imminence of the outcome from individual choices. However, human economic research has developed a simple auction-like procedure that can reveal in a direct and immediate manner the true subjective value in individual choices [Becker–DeGroot–Marschak (BDM) mechanism]. The current study implemented this mechanism in rhesus monkeys and demonstrates its usefulness for eliciting meaningful value estimates of liquid rewards. The mechanism allows future neurophysiological assessment of subjective reward value signals in single trials of controlled animal tasks.
Thematic Ideation: A Superior Supplementary Concept in Creativity and Innovation

New products improve competitiveness through their creativity and innovativeness. Creativity and innovativeness are parallel yet non-identical concepts; newly introduced products are either somewhat thematically similar or taxonomically similar to the existing products. There is a need for the explication of creativity and innovativeness as separate or as unified concepts in newly developed products, in particular, thematic products. A mixed-method design was adopted to establish definitions and components of creativity and innovativeness in the ideas presented at the innovation summit through 489 perceptual reports on 14 feature-based and theme-based product ideas. On the whole, three components—originality, value, and commercial appeal—were derived and tested. Results revealed the significance of originality as a major component of creativity and innovativeness in all products. Perceived value was significantly related to originality in both taxonomic and thematic products. Originality and value predicted creativity whereas innovativeness was predicted by commercial appeal along with originality and value in all products. The underlying purchase intention in taxonomic and thematic product ideas was the product’s relatedness with lifestyle. A product idea to be implemented in business as innovation was found to be dependent on its creativity and commercial appeal. This asserted three factors mandatory at the Fuzzy Front End (FFE), namely, originality, value, and commercial appeal. Discussion includes the interpretation of results and future directions.
Studies in the Rubiaceae - Rubioideae for the 'Flora of Tropical East Africa' : I

In dealing with the Psychotria peduncularis (Salisb.) Steyermark complex (as Cephaelis peduncularis Salisb.), Hepper (Fl. W. Trop. Afr. ed. 2, 2: 203-4 (1963)) had to recognize numerous varieties. A similar treatment has been found necessary but to a lesser degree in dealing with the species in East Africa. As the first paper in this series was going to press I became dissatisfied with several of my conclusions concerning Psychotria section Involucratae Petit & Verdc. and deleted parts of the account. My revised opinions are given here. There is no doubt in my mind that many of the numerous 'species' described invalidly by De Wildeman under Uragoga (in M6m. Inst. Roy. Col. Belge, Sect. Sci. Nat. M6d. 8' 4 (4): 87-I87 (1936)) are merely variants of P. peduncularis. Two of the names fit taxa occurring in East Africa.
Understanding Software Changes: Extracting, Classifying, and Presenting Fine-Grained Source Code Changes

In modern software engineering, developers have to work with constantly evolving, interconnected software systems. Understanding how and why these systems and their dependencies between each other change is therefore an essential step in improving or maintaining them. For this, it is important to know what changed and how these changes influence the system. Most currently used tools that help developers to understand source code changes either use the textual representation of source code, allowing for a coarse-grained overview, or use the AST (abstract syntax tree) representation of source code to extract more fine-grained changes. We plan to improve the accuracy and classification of the extracted source code changes and to extend them by analysing the fine-grained changes of source code dependencies. We also propose a dynamical analysis of the impact of the previously extracted changes on performance metrics. This helps to understand what changes caused a certain change in program behaviour. We plan to use and combine this information to generate accurate and detailed change overviews that bridge the gap between existing coarse-grained solutions and the raw changes contained in the code, aiming to reduce the developers’ time spent reading changed code and help them to quickly understand the changes between two versions of source code.
Seasonal Variability in the Microbial Plankton Community in a Semienclosed Bay Affected by Upwelling: The Role of a Nutrient Trap

The Ría de Vigo is a semienclosed bay in northwestern Iberia that is affected by seasonal upwelling (spring to autumn) that triggers high levels of primary productivity. Fisheries and aquaculture coexist with other human activities, such as shipbuilding and tourism, in this area. The microbial plankton community in this system was studied to understand how this community is structured and progresses seasonally. Autotrophic biomass (AB) dominated from spring to autumn when upwelling prevailed, while AB and heterotrophic biomass (HB) covaried throughout the year as HB:AB = 0.26 ± 0.04 (r2 = 0.7), indicating a link between the two communities. Diatoms and autotrophic nanoflagellates accounted for 80 ± 18% of the AB, whereas the HB was mainly composed of heterotrophic bacteria (36 ± 11%) and heterotrophic nanoflagellates (30 ± 12%). The AB and HB, as well as their main components, showed a continuous increase during the upwelling season that was attributed to growth. However, a sudden increase occurred in autumn, when downwelling caused the biomass to accumulate before leading to the low values in winter. As upwelling promotes the export of materials to the open ocean, the increase in biomass was possibly due to the existence of a nutrient trap that fueled phytoplankton growth through recycling at the sediment-water interface a substantial portion of the organic matter previously synthetized in the water column. The reorganization of the community that followed the winter decrease in biomass began with the proliferation of small plankton forms.
What disconnects Practitioner Belief and Empirical Evidence ?

Just because software developers say they believe in "X", that does not necessarily mean that "X" is true. As shown here, there exist numerous beliefs listed in the recent Software Engineering literature which are only supported by small portions of the available data. Hence we ask what is the source of this disconnect between beliefs and evidence? To answer this question we look for evidence for ten beliefs within 300,000+ changes seen in dozens of open-source projects. Some of those beliefs had strong support across all the projects; specifically, "A commit that involves more added and removed lines is more bug-prone" and "Files with fewer lines contributed by their owners (who contribute most changes) are bug-prone". Most of the widely-held beliefs studied are only sporadically supported in the data; i.e. large effects can appear in project data and then disappear in subsequent releases. Such sporadic support explains why developers believe things that were relevant to their prior work, but not necessarily their current work.
Semantic Analysis of Issues on Google Play and Twitter

Mobile app users post their opinion about the apps, report bugs or request features on various platforms, the main one being App Stores. Previous research suggests that Twitter should be used as an additional resource to receive users' feedback, as app users tweet different issues. Although the classification and review summarization methods are developed previously for each platform separately, manual investigation of reviews or tweets is still required to identify the similar or different points that are discussed on App Store or Twitter. In this paper, we propose a framework to study the differences or similarities among app reviews from Google Play Store and tweets automatically by using the semantics of the words. The results from several experiments compared with expert evaluation, confirm that it can be applied to identify the similarities or differences among the extracted topics, n-grams, and users' comments.
Adapting Requirements Models to Varying Environments

The engineering of high-quality software requirements generally relies on properties and assumptions about the environment in which the software-to-be has to operate. Such properties and assumptions, referred to as environment conditions in this paper, are highly subject to change over time or from one software variant to another. As a consequence, the requirements engineered for a specific set of environment conditions may no longer be adequate, complete and consistent for another set. The paper addresses this problem through a tool-supported requirements adaptation technique. A goal-oriented requirements modelling framework is considered to make requirements' refinements and dependencies on environment conditions explicit. When environment conditions change, an adapted goal model is computed that is correct with respect to the new environment conditions. The space of possible adaptations is not fixed a priori; the required changes are expected to meet one or more environment-independent goal(s) to be satisfied in any version of the system. The adapted goal model is generated using a new counterexample-guided learning procedure that ensures the correctness of the updated goal model, and prefers more local adaptations and more similar goal models.
Revealing Injection Vulnerabilities by Leveraging Existing Tests

Code injection attacks, like the one used in the high-profile 2017 Equifax breach, have become increasingly common, now ranking #1 on OWASP's list of critical web application vulnerabilities. Static analyses for detecting these vulnerabilities can overwhelm developers with false positive reports. Meanwhile, most dynamic analyses rely on detecting vulnerabilities as they occur in the field, which can introduce a high performance overhead in production code. This paper describes a new approach for detecting injection vulnerabilities in applications by harnessing the combined power of human developers' test suites and automated dynamic analysis. Our new approach, Rivulet, monitors the execution of developer-written functional tests in order to detect information flows that may be vulnerable to attack. Then, Rivulet uses a white-box test generation technique to repurpose those functional tests to check if any vulnerable flow could be exploited. When applied to the version of Apache Struts exploited in the 2017 Equifax attack, Rivulet quickly identifies the vulnerability, leveraging only the tests that existed in Struts at that time. We compared Rivulet to the state-of-the-art static vulnerability detector Julia on benchmarks, finding that Rivulet outperformed Julia in both false positives and false negatives. We also used Rivulet to detect new vulnerabilities.
The Same and the Different: On Semantization and Instrumentalization Practices in the (Maths) Classroom

There is no education without content. Teaching and learning in schools is devoted to “something,” not to “nothing” or “anything. ” The more heterogeneous, multicultural, and multilingual the classroom, the more semantization of educational content within teaching and learning is needed. Therefore, the role of various languages in the classroom is revisited. In this article, we elaborate on the theory of language-sensitive content transformation and introduce the concept of operational isomorphism and its potential in the field of didactics, research on teaching, and learning and teacher education. Using a case study of mathematics teaching, we illustrate, how students can benefit from semantization and instrumentalization practices in the classroom and how these processes take the form of extensional deconstruction and intensional expansion or intensional condensation.
Calibration method of shear amount based on the optical layout of point source microscope for lateral shearing interferometric wavefront sensor

Abstract. A calibration method of shear amount based on the optical layout of point source microscope (PSM) for lateral shearing interferometric (LSI) wavefront sensor is proposed. A simulation model of quadriwave LSI is introduced to analyze the influence of the window size of the spatial filter and the shear amount on the accuracy of the shearing wavefront feature extraction (SWFE) method. Simulation results show that the accuracy of the SWFE method deteriorates at small shear amounts. The proposed calibration method makes use of the optical layout of PSM to generate spot images of the point source at the detector plane of the sensor itself. The shear amount is calibrated by the relationship between the lateral distance of the spot images and the distribution of the diffraction orders of the grating. In the experiments, an SID4 wavefront sensor and a circular-aperture modified Hartmann mask LSI wavefront sensor are calibrated by the proposed method. A phase plate etched with patterns has been manufactured as the test specimen. The etching depth is characterized by the two wavefront sensors, and the testing results are compared with that by a commercial ZYGO® interferometer. The feasibility and accuracy of the proposed calibration method are validated. This calibration method provides an easily conducted approach for calibrating the shear amount of LSI wavefront sensor with high accuracy.
Estimation of multimode pump reliability in radiation environment using Monte Carlo simulation

Abstract. High powered optical amplifiers (HPOAs) are a key subassembly in free-space optical communication systems. As these free-space optical communications systems are being introduced into space applications, it is critical to assess the impact of the radiation environment present in space on the reliability of these systems. One key component in HPOAs is multimode laser diodes, which are used to pump gain fibers in the HPOA. The reliability of these laser diodes is very dependent on optical output power, with increases in optical power causing dramatic reductions in lifetime. At the same time, radiation darkening is a well-known problem that reduces the efficiency in HPOAs operating in environments with high radiation levels. We use Monte Carlo simulation to evaluate the impact of radiation darkening on the reliability of multimode pump ensembles over time. At radiation fields related to geostationary earth orbits (2 to 5 krad / year) radiation effects reduce multimode pump lifetime by a factor of 7-220 under typical use conditions. Use of redundant pumps operating under derated conditions can mitigate the impact of radiation.
Poster: How Has Forking Changed in the Last 20 Years? A Study of Hard Forks on GitHub

The notion of forking has changed with the rise of distributed version control systems and social coding environments, like GitHub. Traditionally forking refers to splitting off an independent development branch (which we call hard forks); research on hard forks, conducted mostly in pre-GitHub days showed that hard forks were often seen critical as they may fragment a community. Today, in social coding environments, open-source developers are encouraged to fork a project in order to contribute to the community (which we call social forks), which may have also influenced perceptions and practices around hard forks. To revisit hard forks, we identify, study, and classify 15,306 hard forks on GitHub and interview 18 owners of hard forks or forked repositories. We find that, among others, hard forks often evolve out of social forks rather than being planned deliberately and that perception about hard forks have indeed changed dramatically, seeing them often as a positive noncompetitive alternative to the original project.