nils-herrmann/clean_scirepeval huggingface dataset:
Grab 'n Run: Secure and Practical Dynamic Code Loading for Android Applications

Android introduced the dynamic code loading (DCL) mechanism to allow for code reuse, to achieve extensibility, to enable updating functionalities, or to boost application start-up performance. In spite of its wide adoption by developers, previous research has shown that the secure implementation of DCL-based functionality is challenging, often leading to remote code injection vulnerabilities. Unfortunately, previous attempts to address this problem by both the academic and Android developers communities are affected by either practicality or completeness issues, and, in some cases, are affected by severe vulnerabilities.
 In this paper, we propose, design, implement, and test Grab 'n Run, a novel code verification protocol and a series of supporting libraries, APIs, and tools, that address the problem by abstracting away from the developer many of the challenging implementation details. Grab 'n Run is designed to be practical: Among its tools, it provides a drop-in library, which requires no modifications to the Android framework or the underlying Dalvik/ART runtime, is very similar to the native API, and most code can be automatically rewritten to use it. Grab 'n Run also contains an application-rewriting tool, which allows to easily port legacy or third-party applications to use the secure APIs developed in this work.
 We evaluate the Grab 'n Run library with a user study, obtaining very encouraging results in vulnerability reduction, ease of use, and speed of development. We also show that the performance overhead introduced by our library is negligible. For the benefit of the security of the Android ecosystem, we released Grab 'n Run as open source.
Depth-aware neural style transfer

Neural style transfer has recently received significant attention and demonstrated amazing results. An efficient solution proposed by Johnson et al. trains feed-forward convolutional neural networks by defining and optimizing perceptual loss functions. Such methods are typically based on high-level features extracted from pre-trained neural networks, where the loss functions contain two components: style loss and content loss. However, such pre-trained networks are originally designed for object recognition, and hence the high-level features often focus on the primary target and neglect other details. As a result, when input images contain multiple objects potentially at different depths, the resulting images are often unsatisfactory because image layout is destroyed and the boundary between the foreground and background as well as different objects becomes obscured. We observe that the depth map effectively reflects the spatial distribution in an image and preserving the depth map of the content image after stylization helps produce an image that preserves its semantic content. In this paper, we introduce a novel approach for neural style transfer that integrates depth preservation as additional loss, preserving overall image layout while performing style transfer.
The Effect of Private IT Use on Work Performance - Towards an IT Consumerization Theory

IT consumerization – defined as the use of privately-owned IT resources for business purposes – is steadily growing, thus creating new challenges for enterprises. While numerous practitioner studies suggest a positive effect of this trend on employee work performance, IS research still lacks a systematic understanding of the forces underlying this relationship. In order to close this research gap, we derive three major effects of IT consumerization on employees: 1) an increased workload 2) an elevated autonomy and 3) a higher level of competence. Drawing on cognitive stress model and self-determination theory, we develop an innovative theoretical model of the relationships between IT consumerization and work performance. We then conduct an embedded singlecase study, in order to evaluate the constructs and relationships of our structural model by means of qualitative research. Subsequently, the implications for th organizing and practicing IT consumerization are discussed and suggestions on further developing this study are presented.
Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market

We analyze how network effects affect competition in the nascent cryptocurrency market. We do so by examining early dynamics of exchange rates among different cryptocurrencies. While Bitcoin essentially dominates this market, our data suggest no evidence of a winner-take-all effect early in the market. Indeed, for a relatively long period, a few other cryptocurrencies competing with Bitcoin (the early industry leader) appreciated much more quickly than Bitcoin. The data in this period are consistent with the use of cryptocurrencies as financial assets (popularized by Bitcoin), and not consistent with winner-take-all dynamics. Toward the end of our sample, however, things change dramatically. Bitcoin appreciates against the USD, while other currencies depreciate against the USD. The data in this period are consistent with strong network effects and winner-take-all dynamics. This trend continues at the time of writing.
Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs)

Massive open online courses (MOOCs) are now being used across the world to provide millions of learners with access to education. Many learners complete these courses successfully, or to their own satisfaction, but the high numbers who do not finish remain a subject of concern for platform providers and educators. In 2013, a team from Stanford University analysed engagement patterns on three MOOCs run on the Coursera platform. They found four distinct patterns of engagement that emerged from MOOCs based on videos and assessments. However, not all platforms take this approach to learning design. Courses on the FutureLearn platform are underpinned by a social-constructivist pedagogy, which includes discussion as an important element. In this paper, we analyse engagement patterns on four FutureLearn MOOCs and find that only two clusters identified previously apply in this case. Instead, we see seven distinct patterns of engagement: Samplers, Strong Starters, Returners, Mid-way Dropouts, Nearly There, Late Completers and Keen Completers. This suggests that patterns of engagement in these massive learning environments are influenced by decisions about pedagogy. We also make some observations about approaches to clustering in this context.
Face Liveness Detection From a Single Image via Diffusion Speed Model

Spoofing using photographs or videos is one of the most common methods of attacking face recognition and verification systems. In this paper, we propose a real-time and nonintrusive method based on the diffusion speed of a single image to address this problem. In particular, inspired by the observation that the difference in surface properties between a live face and a fake one is efficiently revealed in the diffusion speed, we exploit antispoofing features by utilizing the total variation flow scheme. More specifically, we propose defining the local patterns of the diffusion speed, the so-called local speed patterns, as our features, which are input into the linear SVM classifier to determine whether the given face is fake or not. One important advantage of the proposed method is that, in contrast to previous approaches, it accurately identifies diverse malicious attacks regardless of the medium of the image, e.g., paper or screen. Moreover, the proposed method does not require any specific user action. Experimental results on various data sets show that the proposed method is effective for face liveness detection as compared with previous approaches proposed in studies in the literature.
Men who kill their own children: paternal filicide incidents in contemporary Fiji.

Filicide, or parental murder of offspring, constitutes a major portion of lethal violence perpetrated against children worldwide. Despite the global nature of the phenomenon, researchers have focused their studies on the developed industrialized societies with the consequent neglect of small, developing societies. Second, there is a paucity of empirical data on child homicide committed by fathers. This study therefore explores the nature and extent of paternal filicides in Fiji, a non-Western society, and the social and cultural forces underlying them in order to enhance our knowledge of the phenomenon.



Information was obtained from a number of sources, including (a) a police homicide logbook, (b) newspaper reports of homicide, and (c) detailed interviews conducted with criminal justice and medical personnel. Information from these data sources were consolidated to construct case histories of paternal filicides. These cases were then analyzed for dominant themes. Case illustrations are presented in the text.



Several of the study's findings are congruent with other studies of paternal filicides: poor, working class fathers were the offenders in all cases. As a corollary, their victims were from low socioeconomic backgrounds. Regarding location, paternal filicides occurred in the home of the offender and victim. The filicides were the culmination of stresses and strains associated with marital disharmony and excessive corporal child-control strategies.



The general conclusion of this study is that further research in non-Western societies has the potential to increase our understanding of the social factors and processes involved in paternal child murders. We will then be better positioned to craft effective intervention strategies.
A One-Phase Algorithm to Detect Distributed Deadlocks in Replicated Databases

Replicated databases that use quorum-consensus algorithms to perform majority voting are prone to deadlocks. Due to the P-out-of-Q nature of quorum requests, deadlocks that arise are generalized deadlocks and are hard to detect. We present an efficient distributed algorithm to detect generalized deadlocks in replicated databases. The algorithm performs reduction of a distributed waitfor-graph (WFG) to determine the existence of a deadlock. if sufficient information to decide the reducibility of a node is not available at that node, the algorithm attempts reduction later in a lazy manner. We prove the correctness of the algorithm. The algorithm has a message complexity of 2n messages and a worst-case time complexity of 2d + 2 hops, where c is the number of edges and d is the diameter of the WFG. The algorithm is shown to perform significantly better in both time and message complexity than the best known existing algorithms. We conjecture that this is an optimal algorithm, in time and message complexity, to detect generalized deadlocks if no transaction has complete knowledge of the topology of the WFG or the system and the deadlock detection is to be carried out in a distributed manner.
Visualizing Instagram: Tracing Cultural Visual Rhythms

Picture-taking has never been easier. We now use our phones to snap photos and instantly share them with friends, family and strangers all around the world. Consequently, we seek ways to visualize, analyze and discover concealed sociocultural characteristics and trends in this ever-growing flow of visual information. How do we then trace global and local patterns from the analysis of visual planetary–scale data? What types of insights can we draw from the study of these massive visual materials? In this study we use Cultural Analytics visualization techniques for the study of approximately 550,000 images taken by users of the location-based social photo sharing application Instagram. By analyzing images from New York City and Tokyo, we offer a comparative visualization research that indicates differences in local color usage, cultural production rate, and varied hue’s intensities— all form a unique, local, ‘Visual Rhythm’: a framework for the analysis of location-based visual information flows.
Gas Sensors Based on Conducting Polymers

The gas sensors fabricated by using conducting polymers such as polyaniline (PAni), polypyrrole (PPy) and poly (3,4-ethylenedioxythiophene) (PEDOT) as the active layers have been reviewed. This review discusses the sensing mechanism and configurations of the sensors. The factors that affect the performances of the gas sensors are also addressed. The disadvantages of the sensors and a brief prospect in this research field are discussed at the end of the review.
Robust camera calibration for sport videos using court models

We propose an automatic camera calibration algorithm for court sports. The obtained camera calibration parameters are required for applications that need to convert positions in the video frame to real-world coordinates or vice versa. Our algorithm uses the line markings of the court for calibration and it can be applied to a variety of different sports since the geometric model of the court can be specified by the user. The algorithm starts with a model initialization step which locates the court in the image without any user assistance or a-priori knowledge about the most probable position. Image pixels are classified as court line pixels if they pass several tests including color and local texture restrictions. A Hough transform is applied to extract line elements, forming a set of court line candidates. The subsequent combinatorial search establishes correspondences between lines in the input image and lines from the court model. For the following input frames, an abbreviated calibration algorithm is used, which predicts the camera parameters for the new image and optimizes the parameters using a gradient descent algorithm. We have conducted experiments on a variety of sport videos (tennis, volleyball, and goal area sequences of soccer games). Video scenes with considerable difficulties were selected to test the robustness of the algorithm. Results show that the algorithm is very robust to occlusions, partial court views, bad lighting conditions, or shadows.
Calibration between Color Camera and 3D LIDAR Instruments with a Polygonal Planar Board

Calibration between color camera and 3D Light Detection And Ranging (LIDAR) equipment is an essential process for data fusion. The goal of this paper is to improve the calibration accuracy between a camera and a 3D LIDAR. In particular, we are interested in calibrating a low resolution 3D LIDAR with a relatively small number of vertical sensors. Our goal is achieved by employing a new methodology for the calibration board, which exploits 2D-3D correspondences. The 3D corresponding points are estimated from the scanned laser points on the polygonal planar board with adjacent sides. Since the lengths of adjacent sides are known, we can estimate the vertices of the board as a meeting point of two projected sides of the polygonal board. The estimated vertices from the range data and those detected from the color image serve as the corresponding points for the calibration. Experiments using a low-resolution LIDAR with 32 sensors show robust results.
Multiple Choice Learning: Learning to Produce Multiple Structured Outputs

We address the problem of generating multiple hypotheses for structured prediction tasks that involve interaction with users or successive components in a cascaded architecture. Given a set of multiple hypotheses, such components/users typically have the ability to retrieve the best (or approximately the best) solution in this set. The standard approach for handling such a scenario is to first learn a single-output model and then produce M -Best Maximum a Posteriori (MAP) hypotheses from this model. In contrast, we learn to produce multiple outputs by formulating this task as a multiple-output structured-output prediction problem with a loss-function that effectively captures the setup of the problem. We present a max-margin formulation that minimizes an upper-bound on this lossfunction. Experimental results on image segmentation and protein side-chain prediction show that our method outperforms conventional approaches used for this type of scenario and leads to substantial improvements in prediction accuracy.
Enhancing Differential Evolution Utilizing Eigenvector-Based Crossover Operator

Differential evolution has been shown to be an effective methodology for solving optimization problems over continuous space. In this paper, we propose an eigenvector-based crossover operator. The proposed operator utilizes eigenvectors of covariance matrix of individual solutions, which makes the crossover rotationally invariant. More specifically, the donor vectors during crossover are modified, by projecting each donor vector onto the eigenvector basis that provides an alternative coordinate system. The proposed operator can be applied to any crossover strategy with minimal changes. The experimental results show that the proposed operator significantly improves DE performance on a set of 54 test functions in CEC 2011, BBOB 2012, and CEC 2013 benchmark sets.
A Simple, Low-Cost Conductive Composite Material for 3D Printing of Electronic Sensors

3D printing technology can produce complex objects directly from computer aided digital designs. The technology has traditionally been used by large companies to produce fit and form concept prototypes ('rapid prototyping') before production. In recent years however there has been a move to adopt the technology as full-scale manufacturing solution. The advent of low-cost, desktop 3D printers such as the RepRap and Fab@Home has meant a wider user base are now able to have access to desktop manufacturing platforms enabling them to produce highly customised products for personal use and sale. This uptake in usage has been coupled with a demand for printing technology and materials able to print functional elements such as electronic sensors. Here we present formulation of a simple conductive thermoplastic composite we term 'carbomorph' and demonstrate how it can be used in an unmodified low-cost 3D printer to print electronic sensors able to sense mechanical flexing and capacitance changes. We show how this capability can be used to produce custom sensing devices and user interface devices along with printed objects with embedded sensing capability. This advance in low-cost 3D printing with offer a new paradigm in the 3D printing field with printed sensors and electronics embedded inside 3D printed objects in a single build process without requiring complex or expensive materials incorporating additives such as carbon nanotubes.
Development of whole-body emotion expression humanoid robot

Personal robots and robot technology (RT)-based assistive devices are expected to play a major role in our elderly-dominated society, with an active participation to joint works and community life with humans, as partner and as friends for us. The authors think that the emotion expression of a robot is effective in joint activities of human and robot. In addition, we also think that bipedal walking is necessary to robots which are active in human living environment. But, there was no robot which has those functions. And, it is not clear what kinds of functions are effective actually. Therefore we developed a new bipedal walking robot which is capable to express emotions. In this paper, we present the design and the preliminary evaluation of the new head of the robot with only a small number of degrees of freedom for facial expression.
Work experiences and personality development in young adulthood.

This longitudinal study provides an analysis of the relationship between personality traits and work experiences with a special focus on the relationship between changes in personality and work experiences in young adulthood. Longitudinal analyses uncovered 3 findings. First, measures of personality taken at age 18 predicted both objective and subjective work experiences at age 26. Second, work experiences were related to changes in personality traits from age 18 to 26. Third, the predictive and change relations between personality traits and work experiences were corresponsive: Traits that "selected" people into specific work experiences were the same traits that changed in response to those same work experiences. The relevance of the findings to theories of personality development is discussed.
Weakly Supervised Dense Video Captioning

This paper focuses on a novel and challenging vision task, dense video captioning, which aims to automatically describe a video clip with multiple informative and diverse caption sentences. The proposed method is trained without explicit annotation of fine-grained sentence to video region-sequence correspondence, but is only based on weak video-level sentence annotations. It differs from existing video captioning systems in three technical aspects. First, we propose lexical fully convolutional neural networks (Lexical-FCN) with weakly supervised multi-instance multi-label learning to weakly link video regions with lexical labels. Second, we introduce a novel submodular maximization scheme to generate multiple informative and diverse region-sequences based on the Lexical-FCN outputs. A winner-takes-all scheme is adopted to weakly associate sentences to region-sequences in the training phase. Third, a sequence-to-sequence learning based language model is trained with the weakly supervised information obtained through the association process. We show that the proposed method can not only produce informative and diverse dense captions, but also outperform state-of-the-art single video captioning methods by a large margin.
Content Security Problems?: Evaluating the Effectiveness of Content Security Policy in the Wild

Content Security Policy (CSP) is an emerging W3C standard introduced to mitigate the impact of content injection vulnerabilities on websites. We perform a systematic, large-scale analysis of four key aspects that impact on the effectiveness of CSP: browser support, website adoption, correct configuration and constant maintenance. While browser support is largely satisfactory, with the exception of few notable issues, our analysis unveils several shortcomings relative to the other three aspects. CSP appears to have a rather limited deployment as yet and, more crucially, existing policies exhibit a number of weaknesses and misconfiguration errors. Moreover, content security policies are not regularly updated to ban insecure practices and remove unintended security violations. We argue that many of these problems can be fixed by better exploiting the monitoring facilities of CSP, while other issues deserve additional research, being more rooted into the CSP design.