nils-herrmann/clean_scirepeval huggingface dataset:
Efficient and formal generalized symbolic execution

Programs that manipulate dynamic heap objects are difficult to analyze due to issues like aliasing. Lazy initialization algorithm enables the classical symbolic execution to handle such programs. Despite its successes, there are two unresolved issues: (1) inefficiency; (2) lack of formal study. For the inefficiency issue, we have proposed two improved algorithms that give significant analysis time reduction over the original lazy initialization algorithm. In this article, we formalize the lazy initialization algorithm and the improved algorithms as operational semantics of a core subset of the Java Virtual Machine (JVM) instructions, and prove that all algorithms are relatively sound and complete with respect to the JVM concrete semantics. Finally, we conduct a set of extensive experiments that compare the three algorithms and demonstrate the efficiency of the improved algorithms.
Cultural capital or relative risk aversion? Two mechanisms for educational inequality compared.

In this paper we empirically examined two explanatory mechanisms for educational inequality: cultural reproduction and relative risk aversion, using survey data taken from secondary school pupils in Amsterdam. Cultural reproduction theory seeks to explain class variations in schooling by cultural differences between social classes. Relative risk aversion theory argues that educational inequalities can be understood by between-class variation in the necessity of pursuing education at branching points in order to avoid downward mobility. We showed that class variations in early demonstrated ability are for a substantial part cultural: cultural capital - measured by parental involvement in highbrow culture - affected school performance at the primary and secondary level. However, relative risk aversion - operationalized by being concerned with downward mobility - strongly affects schooling ambitions, whereas cultural capital had no effect. Thus, we conclude that 'primary effects' of social origin on schooling outcomes are manifested through cultural capital and not through relative risk aversion (in addition to other potential sources of class variations such as genetics). Relative risk aversion, and not cultural capital, affects schooling ambitions, which is relevant for our understanding of secondary effects.
Detecting Phishing Websites using Automation of Human Behavior

In this paper, we propose a technique to detect phishing attacks based on behavior of human when exposed to fake website. Some online users submit fake credentials to the login page before submitting their actual credentials. He/She observes the login status of the resulting page to check whether the website is fake or legitimate. We automate the same behavior with our application (FeedPhish) which feeds fake values into login page. If the web page logs in successfully, it is classified as phishing otherwise it undergoes further heuristic filtering. If the suspicious site passes through all heuristic filters then the website is classified as a legitimate site. As per the experimentation results, our application has achieved a true positive rate of 97.61%, true negative rate of 94.37% and overall accuracy of 96.38%. Our application neither demands third party services nor prior knowledge like web history, whitelist or blacklist of URLS. It is able to detect not only zero-day phishing attacks but also detects phishing sites which are hosted on compromised domains.
pubmed.mineR: An R package with text-mining algorithms to analyse PubMed abstracts

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, ‘Evolving role of diabetes educators’, ‘Cancer risk assessment’ and ‘Dynamic concepts on disease and comorbidity’ to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.r-project.org/web/packages/pubmed.mineR .
Impact of Halo Doping on the Subthreshold Performance of Deep-Submicrometer CMOS Devices and Circuits for Ultralow Power Analog/Mixed-Signal Applications

In addition to its attractiveness for ultralow power applications, analog CMOS circuits based on the subthreshold operation of the devices are known to have significantly higher gain as compared to their superthreshold counterpart. The effects of halo [both double-halo (DH) and single-halo or lateral asymmetric channel (LAC)] doping on the subthreshold analog performance of 100-nm CMOS devices are systematically investigated for the first time with extensive process and device simulations. In the subthreshold region, although the halo doping is found to improve the device performance parameters for analog applications (such as gm/Id, output resistance and intrinsic gain) in general, the improvement is significant in the LAC devices. Low angle of tilt of the halo implant is found to give the best improvement in both the LAC and DH devices. Our results show that the CMOS amplifiers made with the halo implanted devices have higher voltage gain over their conventional counterpart, and a more than 100% improvement in the voltage gain is observed when LAC doping is made on both the p- and n-channel devices of the amplifier
Adolescent coping style and behaviors: conceptualization and measurement.

The developmental tasks associated with adolescence pose a unique set of stressors and strains. Included in the normative tasks of adolescence are developing and identity, differentiating from the family while still staying connected, and fitting into a peer group. The adolescent's adaptation to these and other, often competing demands is achieved through the process of coping which involves cognitive and behavioral strategies directed at eliminating or reducing demands, redefining demands so as to make them more manageable, increasing resources for dealing with demands, and/or managing the tension which is felt as a result of experiencing demands. In this paper, individual copying theory and family stress theory are reviewed to provide a theoretical foundation for assessing adolescent coping. In addition, the development and testing of an adolescent self-report coping inventory, Adolescent Coping Orientation for Problem Experiences (A-COPE) is presented. Gender differences in coping style are presented and discussed. These coping patterns were validated against criterion indices of adolescents' use of cigarettes, liquor, and marijuana using data from a longitudinal study of 505 families with adolescents. The findings are discussed in terms of coping theory and measurement and in terms of adolescent development and substance use.
Motivating mobility: designing for lived motivation in stroke rehabilitation

How to motivate and support behaviour change through design is becoming of increasing interest to the CHI community. In this paper, we present our experiences of building systems that motivate people to engage in upper limb rehabilitation exercise after stroke. We report on participatory design work with four stroke survivors to develop a holistic understanding of their motivation and rehabilitation needs, and to construct and deploy engaging interactive systems that satisfy these. We reflect on the limits of motivational theories in trying to design for the lived experience of motivation and highlight lessons learnt around: helping people articulate what motivates them; balancing work, duty, fun; supporting motivation over time; and understanding the wider social context. From these we identify design guidelines that can inform a toolkit approach to support both scalability and personalisability.
L2 Regularization versus Batch and Weight Normalization

Batch Normalization is a commonly used trick to improve the training of deep neural networks. These neural networks use L2 regularization, also called weight decay, ostensibly to prevent overfitting. However, we show that L2 regularization has no regularizing effect when combined with normalization. Instead, regularization has an influence on the scale of weights, and thereby on the effective learning rate. We investigate this dependence, both in theory, and experimentally. We show that popular optimization methods such as ADAM only partially eliminate the influence of normalization on the learning rate. This leads to a discussion on other ways to mitigate this issue.
COMA-boost: co-operative multi agent AdaBoost

Multi feature space representation is a common practise in computer vision applications. Traditional features such as HOG, SIFT, SURF etc., individually encapsulates certain discriminative cues for visual classification. On the other hand, each layer of a deep neural network generates multi ordered representations. In this paper we present a novel approach for such multi feature representation learning using Adaptive Boosting (AdaBoost). General practise in AdaBoost  is to concatenate components of feature spaces and train base learners to classify examples as correctly/incorrectly classified. We posit that multi feature space learning should be viewed as a derivative of cooperative multi agent learning. To this end, we propose a mathematical framework to leverage performance of base learners over each feature space, gauge a measure of "difficulty" of training space and finally make soft weight updates rather than strict binary weight updates prevalent in regular AdaBoost. This is made possible by periodically sharing of response states by our learner agents in the boosting framework. Theoretically, such soft weight update policy allows infinite combinations of weight updates on training space compared to only two possibilities in AdaBoost. This opens up the opportunity to identify 'more difficult' examples compared to 'less difficult' examples. We test our model on traditional multi feature representation of MNIST handwritten character dataset and 100-Leaves classification challenge. We consistently outperform traditional and variants of multi view boosting in terms of accuracy while margin analysis reveals that proposed method fosters formation of more confident ensemble of learner agents. As an application of using our model in conjecture with deep neural network, we test our model on the challenging task of retinal blood vessel segmentation from fundus images of DRIVE dataset by using kernel dictionaries from layers of unsupervised trained stacked autoencoder network. Our work opens a new avenue of research for combining a popular statistical machine learning paradigm with deep network architectures.
Evaluating Fluency in Human-Robot Collaboration

Collaborative fluency is the coordinated meshing of joint activities between members of a well-synchronized team. We aim to build robotic team members that can work side-byside humans by displaying the kind of fluency that humans are accustomed to from each other. As part of this effort, we have developed a number of metrics to evaluate the level of fluency in human-robot shared-location teamwork. In this paper we discuss issues in measuring fluency, present both subjective and objective metrics that have been used to measure fluency between a human and robot, and report on findings along the proposed metrics.
CoAP for content-centric networks

We analyze the design space for implementing the Constrained Application Protocol (CoAP) within Content-Centric Networks (CCN), identifying several CoAP specific scenarios and seeing how they map to CCNs. We present an evaluation, recommendations for implementations and extensions, and directions for future work. Our key result is that while several protocol features and flows map naturally, some communication patterns are more difficult to capture without modifications or additions to the CCN model. In addition to many conceptual and also non-performance related advantages, our experimental evaluation demonstrates that it is feasible and useful to implement CoAP over CCN, with the performance similar to that of CoAP over UDP over IP, and in several cases outperforming it significantly.
IRON: Functional Encryption using Intel SGX

Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an authorized entity compute on encrypted data, and learn the results in the clear. However, all current cryptographic instantiations for general FE are too impractical to be implemented. We construct IRON, a provably secure, and practical FE system using Intel's recent Software Guard Extensions (SGX). We show that IRON can be applied to complex functionalities, and even for simple functions, outperforms the best known cryptographic schemes. We argue security by modeling FE in the context of hardware elements, and prove that IRON satisfies the security model.
A comparison of perceived and real shoulder-surfing risks between alphanumeric and graphical passwords

Previous research has found graphical passwords to be more memorable than non-dictionary or "strong" alphanumeric passwords. Participants in a prior study expressed concerns that this increase in memorability could also lead to an increased susceptibility of graphical passwords to shoulder-surfing. This appears to be yet another example of the classic trade-off between usability and security for authentication systems. This paper explores whether graphical passwords' increased memorability necessarily leads to risks of shoulder-surfing. To date, there are no studies examining the vulnerability of graphical versus alphanumeric passwords to shoulder-surfing.This paper examines the real and perceived vulnerability to shoulder-surfing of two configurations of a graphical password, Passfaces™[30], compared to non-dictionary and dictionary passwords. A laboratory experiment with 20 participants asked them to try to shoulder surf the two configurations of Passfaces™ (mouse versus keyboard data entry) and strong and weak passwords. Data gathered included the vulnerability of the four authentication system configurations to shoulder-surfing and study participants' perceptions concerning the same vulnerability. An analysis of these data compared the relative vulnerability of each of the four configurations to shoulder-surfing and also compared study participants' real and perceived success in shoulder-surfing each of the configurations. Further analysis examined the relationship between study participants' real and perceived success in shoulder-surfing and determined whether there were significant differences in the vulnerability of the four authentication configurations to shoulder-surfing.Findings indicate that configuring data entry for Passfaces™ through a keyboard is the most effective deterrent to shoulder-surfing in a laboratory setting and the participants' perceptions were consistent with that result. While study participants believed that Passfaces™ with mouse data entry would be most vulnerable to shoulder-surfing attacks, the empirical results found that strong passwords were actually more vulnerable.
Bilayer Phosphorene: Effect of Stacking Order on Bandgap and Its Potential Applications in Thin-Film Solar Cells.

Phosphorene, a monolayer of black phosphorus, is promising for nanoelectronic applications not only because it is a natural p-type semiconductor but also because it possesses a layer-number-dependent direct bandgap (in the range of 0.3 to 1.5 eV). On basis of the density functional theory calculations, we investigate electronic properties of the bilayer phosphorene with different stacking orders. We find that the direct bandgap of the bilayers can vary from 0.78 to 1.04 eV with three different stacking orders. In addition, a vertical electric field can further reduce the bandgap to 0.56 eV (at the field strength 0.5 V/Å). More importantly, we find that when a monolayer of MoS2 is superimposed with the p-type AA- or AB-stacked bilayer phosphorene, the combined trilayer can be an effective solar-cell material with type-II heterojunction alignment. The power conversion efficiency is predicted to be ∼18 or 16% with AA- or AB-stacked bilayer phosphorene, higher than reported efficiencies of the state-of-the-art trilayer graphene/transition metal dichalcogenide solar cells.
Evolution, brain, and the nature of language

Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human.
New trends in gender and mathematics performance: a meta-analysis.

In this article, we use meta-analysis to analyze gender differences in recent studies of mathematics performance. First, we meta-analyzed data from 242 studies published between 1990 and 2007, representing the testing of 1,286,350 people. Overall, d = 0.05, indicating no gender difference, and variance ratio = 1.08, indicating nearly equal male and female variances. Second, we analyzed data from large data sets based on probability sampling of U.S. adolescents over the past 20 years: the National Longitudinal Surveys of Youth, the National Education Longitudinal Study of 1988, the Longitudinal Study of American Youth, and the National Assessment of Educational Progress. Effect sizes for the gender difference ranged between -0.15 and +0.22. Variance ratios ranged from 0.88 to 1.34. Taken together, these findings support the view that males and females perform similarly in mathematics.
An Exact Penalty Method for Binary Optimization Based on MPEC Formulation

Binary optimization is a central problem in mathematical optimization and its applications are abundant. To solve this problem, we propose a new class of continuous optimization techniques, which is based on Mathematical Programming with Equilibrium Constraints (MPECs). We first reformulate the binary program as an equivalent augmented biconvex optimization problem with a bilinear equality constraint, then we propose an exact penalty method to solve it. The resulting algorithm seeks a desirable solution to the original problem via solving a sequence of linear programming convex relaxation subproblems. In addition, we prove that the penalty function, induced by adding the complementarity constraint to the objective, is exact, i.e., it has the same local and global minima with those of the original binary program when the penalty parameter is over some threshold. The convergence of the algorithm can be guaranteed, since it essentially reduces to block coordinate descent in the literature. Finally, we demonstrate the effectiveness of our method on the problem of dense subgraph discovery. Extensive experiments show that our method outperforms existing techniques, such as iterative hard thresholding and linear programming relaxation.
IFCaaS: Information Flow Control as a Service for Cloud Security

With the maturity of service-oriented architecture (SOA) and Web technologies, web services have become critical components of Software as a Service (SaaS) applications in cloud ecosystem environments. Most SaaS applications leverage multi-tenant data stores as a back end to keep and process data with high agility. Although these technologies promise impressive benefits, they put SaaS applications at risk against novel as well as prevalent attack vectors. This security risk is further magnified by the loss of control and lack of security enforcement over sensitive data manipulated by SaaS applications. An effective solution is needed to fulfill several requirements originating in the dynamic and complex nature of such applications. Inspired by the rise of Security as a Service (SecaaS) model, this paper introduces "Information Flow Control as a Service (IFCaaS)". IFCaaS lays the foundation of cloud-delivered IFC-based security analysis and monitoring services. As an example of the adoption of the IFCaaS, this paper presents a novel framework that addresses the detection of information flow vulnerabilities in SaaS applications. Our initial experiments show that the framework is a viable solution to protect against data integrity and confidentiality violations leading to information leakage.
A Comparison of Retrieval Models using Term Dependencies

A number of retrieval models incorporating term dependencies have recently been introduced. Most of these modify existing "bag-of-words" retrieval models by including features based on the proximity of pairs of terms (or bi-terms). Although these term dependency models have been shown to be significantly more effective than the bag-of-words models, there have been no previous systematic comparisons between the different approaches that have been proposed. In this paper, we compare the effectiveness of recent bi-term dependency models over a range of TREC collections, for both short (title) and long (description) queries. To ensure the reproducibility of our study, all experiments are performed on widely available TREC collections, and all tuned retrieval model parameters are made public. These comparisons show that the weighted sequential dependence model is at least as effective as, and often significantly better than, any other model across this range of collections and queries. We observe that dependency features are much more valuable in improving the performance of longer queries than for shorter queries. We then examine the effectiveness of dependence models that incorporate proximity features involving more than two terms. The results show that these features can improve effectiveness, but not consistently, over the available data sets.
A measurement study of google play

Although millions of users download and use third-party Android applications from the Google Play store, little information is known on an aggregated level about these applications. We have built PlayDrone, the first scalable Google Play store crawler, and used it to index and analyze over 1,100,000 applications in the Google Play store on a daily basis, the largest such index of Android applications. PlayDrone leverages various hacking techniques to circumvent Google's roadblocks for indexing Google Play store content, and makes proprietary application sources available, including source code for over 880,000 free applications. We demonstrate the usefulness of PlayDrone in decompiling and analyzing application content by exploring four previously unaddressed issues: the characterization of Google Play application content at large scale and its evolution over time, library usage in applications and its impact on application portability, duplicative application content in Google Play, and the ineffectiveness of OAuth and related service authentication mechanisms resulting in malicious users being able to easily gain unauthorized access to user data and resources on Amazon Web Services and Facebook.