nils-herrmann/clean_scirepeval huggingface dataset:
Improving Code: The (Mis) Perception of Quality Metrics

Code quality metrics are widely used to identify design flaws (e.g., code smells) as well as to act as fitness functions for refactoring recommenders. Both these applications imply a strong assumption: quality metrics are able to assess code quality as perceived by developers. Indeed, code smell detectors and refactoring recommenders should be able to identify design flaws/recommend refactorings that are meaningful from the developer's point-of-view. While such an assumption might look reasonable, there is limited empirical evidence supporting it. We aim at bridging this gap by empirically investigating whether quality metrics are able to capture code quality improvement as perceived by developers. While previous studies surveyed developers to investigate whether metrics align with their perception of code quality, we mine commits in which developers clearly state in the commit message their aim of improving one of four quality attributes: cohesion, coupling, code readability, and code complexity. Then, we use state-of-the-art metrics to assess the change brought by each of those commits to the specific quality attribute it targets. We found that, more often than not the considered quality metrics were not able to capture the quality improvement as perceived by developers (e.g., the developer states "improved the cohesion of class C", but no quality metric captures such an improvement).
What Does My Classifier Learn? A Visual Approach to Understanding Natural Language Text Classifiers

Neural Networks have been utilized to solve various tasks such as image recognition, text classification, and machine translation and have achieved exceptional results in many of these tasks. However, understanding the inner workings of neural networks and explaining why a certain output is produced are no trivial tasks. Especially when dealing with text classification problems, an approach to explain network decisions may greatly increase the acceptance of neural network supported tools. In this paper, we present an approach to visualize reasons why a classification outcome is produced by convolutional neural networks by tracing back decisions made by the network. The approach is applied to various text classification problems, including our own requirements engineering related classification problem. We argue that by providing these explanations in neural network supported tools, users will use such tools with more confidence and also may allow the tool to do certain tasks automatically.
Diverse Physiological Roles of Calcitonin Gene-Related Peptide in Migraine Pathology: Modulation of Neuronal-Glial-Immune Cells to Promote Peripheral and Central Sensitization.

The neuropeptide calcitonin gene-related peptide (CGRP) is implicated in the underlying pathology of migraine by promoting the development of a sensitized state of primary and secondary nociceptive neurons. The ability of CGRP to initiate and maintain peripheral and central sensitization is mediated by modulation of neuronal, glial, and immune cells in the trigeminal nociceptive signaling pathway. There is accumulating evidence to support a key role of CGRP in promoting cross excitation within the trigeminal ganglion that may help to explain the high co-morbidity of migraine with rhinosinusitis and temporomandibular joint disorder. In addition, there is emerging evidence that CGRP facilitates and sustains a hyperresponsive neuronal state in migraineurs mediated by reported risk factors such as stress and anxiety. In this review, the significant role of CGRP as a modulator of the trigeminal system will be discussed to provide a better understanding of the underlying pathology associated with the migraine phenotype.
Brand analysis framework for online marketing: ranking web pages and analyzing popularity of brands on social media

Online marketing is one of the best practices used to establish a brand and to increase its popularity. Advertisements are used in a better way to showcase the company’s product/service and give rise to a worthy online marketing strategy. Posting an advertisement on utilitarian web pages helps to maximize brand reach and get a better feedback. Now-a-days companies are cautious of their brand image on the Internet due to the growing number of Internet users. Since there are billions of Web sites on the Internet, it becomes difficult for companies to really decide where to advertise on the Internet for brand popularity. What if, the company advertise on a page which is visited by less number of the interested (for a particular type of product) users instead of a web page which is visited by more number of the interested users?—this doubt and uncertainty—is a core issue faced by many companies. This research paper presents a Brand analysis framework and suggests some experimental practices to ensure efficiency of the proposed framework. This framework is divided into three components—(1) Web site network formation framework—a framework that forms a Web site network of a specific search query obtained from resultant web pages of three search engines-Google, Yahoo & Bing and their associated web pages; (2) content scraping framework—it crawls the content of web pages existing in the framework-formed Web site network; (3) rank assignment of networked web pages—text edge processing algorithm has been used to find out terms of interest and their occurrence associated with search query. We have further applied sentiment analysis to validate positive or negative impact of the sentences, having the search term and its associated terms (with reference to the search query) to identify impact of web page. Later, on the basis of both—text edge analysis and sentiment analysis results, we assigned a rank to networked web pages and online social network pages. In this research work, we present experiments for ‘Motorola smart phone,’ ‘LG smart phone’ and ‘Samsung smart phone’ as search query and sampled the Web site network of top 20 search results of all three search engines and examined up to 60 search results for each search engine. This work is useful to target the right online location for specific brand marketing. Once the brand knows the web pages/social media pages containing high brand affinity and ensures that the content of high affinity web page/social media page has a positive impact, we advertise at that respective online location. Thus, targeted brand analysis framework for online marketing not only has benefits for the advertisement agencies but also for the customers.
Programming by Examples: PL Meets ML

Programming by Examples (PBE) involves synthesizing intended programs in an underlying domain-specific language from examplebased specifications. PBE systems are already revolutionizing the application domain of data wrangling and are set to significantly impact several other domains including code refactoring. There are three key components in a PBE system. (i) A search algorithm that can efficiently search for programs that are consistent with the examples provided by the user. We leverage a divide-and-conquerbased deductive search paradigm that inductively reduces the problem of synthesizing a program expression of a certain kind that satisfies a given specification into sub-problems that refer to sub-expressions or sub-specifications. (ii) Program ranking techniques to pick an intended program from among the many that satisfy the examples provided by the user. We leverage features of the program structure as well of the outputs generated by the program on test inputs. (iii) User interaction models to facilitate usability and debuggability. We leverage active-learning techniques based on clustering inputs and synthesizing multiple programs. Each of these PBE components leverage both symbolic reasoning and heuristics. We make the case for synthesizing these heuristics from training data using appropriate machine learning methods. This can not only lead to better heuristics, but can also enable easier development, maintenance, and even personalization of a PBE system.
Avatar2: A Multi-Target Orchestration Platform

Dynamic binary analysis techniques play a central role to study the security of software systems and detect vulnerabilities in a broad range of devices and applications. Over the past decade, a variety of different techniques have been published, often alongside the release of prototype tools to demonstrate their effectiveness. Unfortunately, most of those techniques’ implementations are deeply coupled with their dynamic analysis frameworks and are not easy to integrate in other frameworks. Those frameworks are not designed to expose their internal state or their results to other components. This prevents analysts from being able to combine together different tools to exploit their strengths and tackle complex problems which requires a combination of sophisticated techniques. Fragmentation and isolation are two important problems which too often results in duplicated efforts or in multiple equivalent solutions for the same problem – each based on a different programming language, abstraction model, or execution environment. In this paper, we present avatar2, a dynamic multi-target orchestration framework designed to enable interoperability between different dynamic binary analysis frameworks, debuggers, emulators, and real physical devices. Avatar2 allows the analyst to organize different tools in a complex topology and then “move” the execution of binary code from one system to the other. The framework supports the automated transfer of the internal state of the device/application, as well as the configurable forwarding of input/output and memory accesses to physical peripherals or emulated targets. To demonstrate avatar2 usage and versatility, in this paper we present three very different use cases in which we replicate a PLC rootkit presented at NDSS 2017, we test Firefox combining Angr and GDB, and we record the execution of an embedded device firmware using PANDA and OpenOCD. All tools and the three use cases will be released as open source to help other researchers to replicate our experiments and perform their own analysis tasks with avatar2.
Novel Field-Weakening Control Scheme for Permanent-Magnet Synchronous Machines Based on Voltage Angle Control

This paper presents a novel voltage-angle-based field-weakening control scheme appropriate for the operation of permanent-magnet synchronous machines over a wide range of speed. At high rotational speed, the stator voltage is limited by the inverter dc bus voltage. To control the machine torque above the base speed, the proposed method controls the angle of the limited stator voltage by the integration of gain-scheduled q-axis current error. The stability of the drive is increased by a feedback loop, which compensates dynamic disturbances and smoothes the transition into field weakening. The proposed method can fully utilize the available dc bus voltage. Due to its simplicity, it is robust to the variation of machine parameters. Excellent performance of the proposed method is demonstrated through the experiments performed with and without speed and position sensors.
Biomineralization mechanism of gold by zygomycete fungi Rhizopus oryzae.

In recent years, there has been significant progress in the biological synthesis of nanomaterials. However, the molecular mechanism of gold biomineralization in microorganisms of industrial relevance remains largely unexplored. Here we describe the biosynthesis mechanism of gold nanoparticles (AuNPs) in the fungus Rhizopus oryzae .  [Au(III)] to nanoparticulate Au(0) (AuNPs) occurs in both the cell wall and cytoplasmic region of R. oryzae . The average size of the as-synthesized AuNPs is ~15 nm. The biomineralization occurs through adsorption, initial reduction to Au(I), followed by complexation [Au(I) complexes], and final reduction to Au(0). Subtoxic concentrations (up to 130 μM) of AuCl(4)(-) in the growth medium increase growth of R. oryzae and induce two stress response proteins while simultaneously down-regulating two other proteins. The induction increases mycelial growth, protein yield, and AuNP biosynthesis. At higher Au(III) concentrations (>130 μM), both mycelial and protein yield decrease and damages to the cellular ultrastructure are observed, likely due to the toxic effect of Au(III). Protein profile analysis also confirms the gold toxicity on R. oryzae at high concentrations. Sodium dodecyl sulfate polyacrylamide gel electrophoresis analysis shows that two proteins of 45 and 42 kDa participate in gold reduction, while an 80 kDa protein serves as a capping agent in AuNP biosynthesis.
Toward Reference Models of Requirements Traceability

ion in Rationale Models. The design goal of KBSA-ADM was to offer a coherent series of rationale models based on results of the REMAP project (Ramesh and Dhar 1992) for maintaining rationale at different levels of detail.  Simple Rationale Model The model sketched in Figure 19 is used for capturing rationale at a simple level of detail. It links an OBJECT with its RATIONALE. The model in Figure 19 also provides for the explicit representation of ASSUMPTIONS and DEPENDENCIES among them. Thus, using this model, the assumptions providing justifications to the creation of objects can be explicitly identified and reasoned with. As changes in such assumptions are a primary factor in the
Measure of Change : The Adjectival Core of Degree Achievements ∗

Current theories of aspect acknowledge the pervasiveness of verbs of variable telicity, and are designed to account both for why these verbs show such variability and for the complex conditions that give rise to telic and atelic interpretations. Previous work has identified several sets of such verbs, including incremental theme verbs, such as eat and destroy; degree achievements, such as cool and widen; and (a)telic directed motion verbs, such as ascend and descend (see e.g., Dowty 1979; Declerck 1979; Dowty 1991; Krifka 1989, 1992; Tenny 1994; Bertinetto and Squartini 1995; Levin and Rappaport Hovav 1995; Jackendoff 1996; Ramchand 1997; Filip 1999; Hay, Kennedy, and Levin 1999; Rothstein 2003; Borer 2005). As the diversity in descriptive labels suggests, most previous work has taken these classes to embody distinct phenomena and to have distinct lexical semantic analyses. We believe that it is possible to provide a unified analysis in which the behavior of all of these verbs stems from a single shared element of their meanings: a function that measures the degree to which an object changes relative to some scalar dimension over the course of an event. We claim that such ‘measures of change’ are based on the more general kinds of measure functions that are lexicalized in many languages by gradable adjectives, and that map an object to a scalar value that represents the degree to which it manifests some gradable property at a time (see Bartsch and Vennemann 1972,
Auxiliary Objectives for Neural Error Detection Models

We investigate the utility of different auxiliary objectives and training strategies within a neural sequence labeling approach to error detection in learner writing. Auxiliary costs provide the model with additional linguistic information, allowing it to learn general-purpose compositional features that can then be exploited for other objectives. Our experiments show that a joint learning approach trained with parallel labels on in-domain data improves performance over the previous best error detection system. While the resulting model has the same number of parameters, the additional objectives allow it to be optimised more efficiently and achieve better performance.
Information Requirements for National Level Cyber Situational Awareness

As modern societies become more dependent on IT services, the potential impact both of adversarial cyberattacks and non-adversarial service management mistakes grows. This calls for better cyber situational awareness-decision-makers need to know what is going on. The main focus of this paper is to examine the information elements that need to be collected and included in a common operational picture in order for stakeholders to acquire cyber situational awareness. This problem is addressed through a survey conducted among the participants of a national information assurance exercise conducted in Sweden. Most participants were government officials and employees of commercial companies that operate critical infrastructure. The results give insight into information elements that are perceived as useful, that can be contributed to and required from other organizations, which roles and stakeholders would benefit from certain information, and how the organizations work with creating cyber common operational pictures today. Among findings, it is noteworthy that adversarial behavior is not perceived as interesting, and that the respondents in general focus solely on their own organization.
Glutamine, arginine, and leucine signaling in the intestine

Glutamine and leucine are abundant constituents of plant and animal proteins, whereas the content of arginine in foods and physiological fluids varies greatly. Besides their role in protein synthesis, these three amino acids individually activate signaling pathway to promote protein synthesis and possibly inhibit autophagy-mediated protein degradation in intestinal epithelial cells. In addition, glutamine and arginine stimulate the mitogen-activated protein kinase and mammalian target of rapamycin (mTOR)/p70 (s6) kinase pathways, respectively, to enhance mucosal cell migration and restitution. Moreover, through the nitric oxide-dependent cGMP signaling cascade, arginine regulates multiple physiological events in the intestine that are beneficial for cell homeostasis and survival. Available evidence from both in vitro and in vivo animal studies shows that glutamine and arginine promote cell proliferation and exert differential cytoprotective effects in response to nutrient deprivation, oxidative injury, stress, and immunological challenge. Additionally, when nitric oxide is available, leucine increases the migration of intestinal cells. Therefore, through cellular signaling mechanisms, arginine, glutamine, and leucine play crucial roles in intestinal growth, integrity, and function.
On the Reaction to Deprecation of 25,357 Clients of 4+1 Popular Java APIs

Application Programming Interfaces (APIs) are a tremendous resource—that is, when they are stable. Several studies have shown that this is unfortunately not the case. Of those, a large-scale study of API changes in the Pharo Smalltalk ecosystem documented several findings about API deprecations and their impact on API clients. We conduct a partial replication of this study, considering more than 25,000 clients of five popular Java APIs on GitHub. This work addresses several shortcomings of the previous study, namely: a study of several distinct API clients in a popular, statically-typed language, with more accurate version information. We compare and contrast our findings with the previous study and highlight new ones, particularly on the API client update practices and the startling similarities between reaction behavior in Smalltalk and Java.
View Definition and Generalization for Database Integration in a Multidatabase System

Access to a heterogeneous distributed collection of databases can be simplified by providing users with a logically integrated interface or global view. There are two aspects to database integration. Firstly, the local schemas may model objects and relationships differently and, secondly, the databases may contain mutually inconsistent data. This paper identifies several kinds of structural and data inconsistencies that might exist. It describes a versatile view definition facility for the functional data model and illustrates the use of this facility for resolving inconsistencies. In particular, the concept of generalization is extended to this model, and its importance to database integration is emphasized. The query modification algorithm for the relational model is extended to the semantically richer functional data model with generalization.
Cannabis use and mental health in young people: cohort study.

To determine whether cannabis use in adolescence predisposes to higher rates of depression and anxiety in young adulthood.



Seven wave cohort study over six years.



44 schools in the Australian state of Victoria.



A statewide secondary school sample of 1601 students aged 14-15 followed for seven years.



Interview measure of depression and anxiety (revised clinical interview schedule) at wave 7.



Some 60% of participants had used cannabis by the age of 20; 7% were daily users at that point. Daily use in young women was associated with an over fivefold increase in the odds of reporting a state of depression and anxiety after adjustment for intercurrent use of other substances (odds ratio 5.6, 95% confidence interval 2.6 to 12). Weekly or more frequent cannabis use in teenagers predicted an approximately twofold increase in risk for later depression and anxiety (1.9, 1.1 to 3.3) after adjustment for potential baseline confounders. In contrast, depression and anxiety in teenagers predicted neither later weekly nor daily cannabis use.



Frequent cannabis use in teenage girls predicts later depression and anxiety, with daily users carrying the highest risk. Given recent increasing levels of cannabis use, measures to reduce frequent and heavy recreational use seem warranted.
Profit-Based DG Planning Considering Environmental and Operational Issues: A Multiobjective Approach

In this paper, a methodology is presented to find the optimal size, location and technology of distributed generation (DG) units considering economic, technical, and environmental issues simultaneously. This technique can be implemented to both renewable and nonrenewable DGs. From economic perspective, minimizing cost and maximizing profit are investigated separately and compared with each other. In addition, from technical and environmental viewpoints, a complete set of indices are defined. In order to find the optimal solution, multiobjective particle swarm optimization, and fuzzy decision-making techniques are utilized. Furthermore, to evaluate the proposed method more accurately, two scenarios are defined. In fact, the first scenario, which is only based on cost minimization and technical objective functions, plays an introductory role for better comprehension of the second scenario. In the second scenario, which includes the presentation of the methodologies of this paper, the effect of selecting either profit maximization or cost minimization as an economic objective function along with the minimization of technical and environmental objective functions are precisely investigated. By carrying out the simulations on the IEEE 33-bus distribution test system, it is concluded that the cost-based framework is not the most profitable one, whereas the profit-based one may well lead to a more appropriate result from economic viewpoints.