nils-herrmann/clean_scirepeval huggingface dataset:
Abusing Social Networks for Automated User Profiling

Recently, social networks such as Facebook have experienced a huge surge in popularity. The amount of personal information stored in these sites calls for appropriate security precautions to protect this data. In this paper, we describe how we are able to take advantage of a common weakness, namely the fact that an attacker can query the social network for registered e-mail addresses on a large scale. Starting with a list of about 10.4 million email addresses, we were able to automatically identify more than 1.2 million user profiles associated with these addresses. By crawling these profiles, we collect publicly available personal information about each user, which we use for automated profiling (i.e., to enrich the information available from each user). Finally, we propose a number of mitigation techniques to protect the user’s privacy. We have contacted the most popular providers, who acknowledged the threat and are currently implementing our countermeasures. Facebook and XING in particular have recently fixed the problem.
Evaluating CALM M5-based vehicle-to-vehicle communication in various road settings through field trials

Future cooperative Intelligent Transport Systems (ITS) applications aimed to improve safety, efficiency and comfort on our roads put high demands on the underlying wireless communication system. To gain better understanding of the limitations of the 5.9 GHz frequency band and the set of communication protocols for medium range vehicle to vehicle (V2V) communication, a set of field trials with CALM M5 enabled prototypes has been conducted. This paper describes five different real vehicle traffic scenarios covering both urban and rural settings at varying vehicle speeds and under varying line-of-sight (LOS) conditions and discusses the connectivity (measured as Packet Reception Ratio) that could be achieved between the two test vehicles. Our measurements indicate a quite problematic LOS sensitivity that strongly influences the performance of V2V-based applications. We further discuss how the awareness of these context-based connectivity problems can be used to improve the design of possible future cooperative ITS safety applications.
Animating explosions

In this paper, we introduce techniques for animating explosions and their effects. The primary effect of an explosion is a disturbance that causes a shock wave to propagate through the surrounding medium. The disturbance determines the behavior of nearly all other secondary effects seen in explosion. We simulate the propagation of an explosion through the surrounding air using a computational fluid dynamics model based on the equations for compressible, viscous flow. To model the numerically stable formation of shocks along blast wave fronts, we employ an integration method that can handle steep pressure gradients without introducing inappropriate damping. The system includes two-way coupling between solid objects and surrounding fluid. Using this technique, we can generate a variety of effects including shaped explosive charges, a projectile propelled from a chamber by an explosion, and objects damaged by a blast. With appropriate rendering techniques, our explosion model can be used to create such visual effects as fireballs, dust clouds, and the refraction of light caused by a blast wave.
Recurrent Convolutional Neural Network Regression for Continuous Pain Intensity Estimation in Video

Automatic pain intensity estimation possesses a significant position in healthcare and medical field. Traditional static methods prefer to extract features from frames separately in a video, which would result in unstable changes and peaks among adjacent frames. To overcome this problem, we propose a real-time regression framework based on the recurrent convolutional neural network for automatic frame-level pain intensity estimation. Given vector sequences of AAM-warped facial images, we used a slidingwindow strategy to obtain fixed-length input samples for the recurrent network. We then carefully design the architecture of the recurrent network to output continuousvalued pain intensity. The proposed end-to-end pain intensity regression framework can predict the pain intensity of each frame by considering a sufficiently large historical frames while limiting the scale of the parameters within the model. Our method achieves promising results regarding both accuracy and running speed on the published UNBCMcMaster Shoulder Pain Expression Archive Database.
Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

Many material and biological samples in scientific imaging are characterized by nonlocal repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a two-dimensional image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the nonlocal redundancy in images. We adapt a framework, termed plug-and-play priors, to solve these imaging problems in a regularized inversion setting. The power of the plug-and-play approach is that it allows a wide array of modern denoising algorithms to be used as a “prior model” for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the plug-and-play approach, and we use these insights to design a new nonlocal means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.
Why are software projects moving from centralized to decentralized version control systems?

Version control systems are essential for co-ordinating work on a software project. A number of open- and closed-source projects are proposing to move, or have already moved, their source code repositories from a centralized version control system (CVCS) to a decentralized version control system (DVCS). In this paper we summarize the differences between a CVCS and a DVCS, and describe some of the rationales and perceived benefits offered by projects to justify the transition.
Novel bi-arm rolled monopole for UWB applications

This work presents a bi-arm rolled monopole for ultrawide-band (UWB) applications. The roll monopole is constructed by wrapping a planar monopole. The impedance and radiation characteristics of the proposed roll monopole are experimentally compared with a rectangular planar monopole and strip monopole. Furthermore, the transfer responses of transmit-receive antenna systems comprising two identical monopoles are examined across the UWB band. The characteristics of the monopoles are investigated in both time and frequency domains for UWB single-band and multiple-band schemes. The study shows that the proposed bi-arm rolled monopole is capable of achieving broadband and omnidirectional radiation characteristics within 3.1-10.6 GHz for UWB wireless communications.
An automated feedback system for computer organization projects

This paper describes a system, built and refined over the past five years, that automatically analyzes student programs assigned in a computer organization course. The system tests a student's program, then e-mails immediate feedback to the student to assist and encourage the student to continue testing, debugging, and optimizing his or her program. The automated feedback system improves the students' learning experience by allowing and encouraging them to improve their program iteratively until it is correct. The system has also made it possible to add challenging parts to each project, such as optimization and testing, and it has enabled students to meet these challenges. Finally, the system has reduced the grading load of University of Michigan's large classes significantly and helped the instructors handle the rapidly increasing enrollments of the 1990s. Initial experience with the feedback system showed that students depended too heavily on the feedback system as a substitute for their own testing. This problem was addressed by requiring students to submit a comprehensive test suite along with their program and by applying automated feedback techniques to help students learn how to write good test suites. Quantitative iterative feedback has proven to be extremely helpful in teaching students specific concepts about computer organization and general concepts on computer programming and testing.
Hand Gesture Recognition with 3 D Convolutional Neural Networks

Touchless hand gesture recognition systems are becoming important in automotive user interfaces as they improve safety and comfort. Various computer vision algorithms have employed color and depth cameras for hand gesture recognition, but robust classification of gestures from different subjects performed under widely varying lighting conditions is still challenging. We propose an algorithm for drivers’ hand gesture recognition from challenging depth and intensity data using 3D convolutional neural networks. Our solution combines information from multiple spatial scales for the final prediction. It also employs spatiotemporal data augmentation for more effective training and to reduce potential overfitting. Our method achieves a correct classification rate of 77.5% on the VIVA challenge dataset.
A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective

Internet of Things (IoT), which will create a huge network of billions or trillions of “Things” communicating with one another, are facing many technical and application challenges. This paper introduces the status of IoT development in China, including policies, R&D plans, applications, and standardization. With China's perspective, this paper depicts such challenges on technologies, applications, and standardization, and also proposes an open and general IoT architecture consisting of three platforms to meet the architecture challenge. Finally, this paper discusses the opportunity and prospect of IoT.
All Your IFCException Are Belong to Us

Existing designs for fine-grained, dynamic information-flow control assume that it is acceptable to terminate the entire system when an incorrect flow is detected-i.e, they give up availability for the sake of confidentiality and integrity. This is an unrealistic limitation for systems such as long-running servers. We identify public labels and delayed exceptions as crucial ingredients for making information-flow errors recoverable in a sound and usable language, and we propose two new error-handling mechanisms that make all errors recoverable. The first mechanism builds directly on these basic ingredients, using not-a-values (NaVs) and data flow to propagate errors. The second mechanism adapts the standard exception model to satisfy the extra constraints arising from information flow control, converting thrown exceptions to delayed ones at certain points. We prove that both mechanisms enjoy the fundamental soundness property of non-interference. Finally, we describe a prototype implementation of a full-scale language with NaVs and report on our experience building robust software components in this setting.
Integrated Polarization Analyzing CMOS Image Sensor for Material Classification

Material classification is an important application in computer vision. The inherent property of materials to partially polarize the reflected light can serve as a tool to classify them. In this paper, a real-time polarization sensing CMOS image sensor using a wire grid polarizer is proposed. The image sensor consist of an array of 128 × 128 pixels, occupies an area of 5 × 4 mm2 and it has been designed and fabricated in a 180-nm CMOS process. We show that this image sensor can be used to differentiate between metal and dielectric surfaces in real-time due to the different nature in partially polarizing the specular and diffuse reflection components of the reflected light. This is achieved by calculating the Fresnel reflection coefficients, the degree of polarization and the variations in the maximum and minimum transmitted intensities for varying specular angle of incidence. Differences in the physical parameters for various metal surfaces result in different surface reflection behavior, influencing the Fresnel reflection coefficients. It is also shown that the image sensor can differentiate among various metals by sensing the change in the polarization Fresnel ratio.
A dual-band circularly polarized stacked microstrip antenna with single-fed for GPS applications

A single probe feeding stacked microstrip antenna is presented to obtain dual-band circularly polarized (CP) characteristics using double layers of truncated square patches. The antenna operates at both the L1 and L2 frequencies of 1575 and 1227 MHz for the global positioning system (GPS). With the optimized design, the measured axial ratio (AR) bandwidths with the centre frequencies of L1 and L2 are both greater than 50 MHz, while the impedance characteristics within AR bandwidth satisfy the requirement of VSWR less than 2. At L1 and L2 frequencies, the AR measured is 0.7 dB and 0.3 dB, respectively.
The use of plant models in deep learning: an application to leaf counting in rosette plants

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.
Large-scale automated synthesis of human functional neuroimaging data

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
Representation and Exchange of Knowledge About Actions, Objects, and Environments in the RoboEarth Framework

The community-based generation of content has been tremendously successful in the World-Wide Web - people help each other by providing information that could be useful to others. We are trying to transfer this approach to robotics in order to help robots acquire the vast amounts of knowledge needed to competently perform everyday tasks. RoboEarth is intended to be a web community by robots for robots to autonomously share descriptions of tasks they have learned, object models they have created, and environments they have explored. In this paper, we report on the formal language we developed for encoding this information and present our approaches to solve the inference problems related to finding information, to determining if information is usable by a robot, and to grounding it on the robot platform.
Self-efficacy beliefs as shapers of children's aspirations and career trajectories.

This prospective study tested with 272 children a structural model of the network of sociocognitive influences that shape children's career aspirations and trajectories. Familial socioeconomic status is linked to children's career trajectories only indirectly through its effects on parents' perceived efficacy and academic aspirations. The impact of parental self-efficacy and aspirations on their children's perceived career efficacy and choice is, in turn, entirely mediated through the children's perceived efficacy and academic aspirations. Children's perceived academic, social, and self-regulatory efficacy influence the types of occupational activities for which they judge themselves to be efficacious both directly and through their impact on academic aspirations. Perceived occupational self-efficacy gives direction to the kinds of career pursuits children seriously consider for their life's work and those they disfavor. Children's perceived efficacy rather than their actual academic achievement is the key determinant of their perceived occupational self-efficacy and preferred choice of worklife. Analyses of gender differences reveal that perceived occupational self-efficacy predicts traditionality of career choice.
Learning What Data to Learn

Machine learning is essentially the sciences of playing with data. An adaptive data selection strategy, enabling to dynamically choose different data at various training stages, can reach a more effective model in a more efficient way. In this paper, we propose a deep reinforcement learning framework, which we call Neural Data Filter (NDF), to explore automatic and adaptive data selection in the training process. In particular, NDF takes advantage of a deep neural network to adaptively select and filter important data instances from a sequential stream of training data, such that the future accumulative reward (e.g., the convergence speed) is maximized. In contrast to previous studies in data selection that is mainly based on heuristic strategies, NDF is quite generic and thus can be widely suitable for many machine learning tasks. Taking neural network training with stochastic gradient descent (SGD) as an example, comprehensive experiments with respect to various neural network modeling (e.g., multi-layer perceptron networks, convolutional neural networks and recurrent neural networks) and several applications (e.g., image classification and text understanding) demonstrate that NDF powered SGD can achieve comparable accuracy with standard SGD process by using less data and fewer iterations.
Hacking Mobile Phones Using 2 D Printed Fingerprints

Fingerprint is the most popular biometric trait due to the perceived uniqueness and persistence of friction ridge pattern on human fingers . Following the introduction of iPhone 5S with Touch ID fingerprint sensor in September 2013, most of the mobile phones, such as iPhone 5s/6/6+, Samsung Galaxy S5/S6, HTC One Max, Huawei Honor 7, Meizu MX4 Pro and others, now come with embedded fingerprint sensors for phone unlock. It has been forecasted that 50% of smartphones sold by 2019 will have an embedded fingerprint sensor . With the introduction of Apple Pay, Samsung Pay and Android Pay, fingerprint recognition on mobile devices is leveraged for more than just for device unlock; it can also be used for secure mobile payment and other transactions.
Using Convolutional Neural Networks to Classify Hate-Speech

The paper introduces a deep learningbased Twitter hate-speech text classification system. The classifier assigns each tweet to one of four predefined categories: racism, sexism, both (racism and sexism) and non-hate-speech. Four Convolutional Neural Network models were trained on resp. character 4-grams, word vectors based on semantic information built using word2vec, randomly generated word vectors, and word vectors combined with character n-grams. The feature set was down-sized in the networks by maxpooling, and a softmax function used to classify tweets. Tested by 10-fold crossvalidation, the model based on word2vec embeddings performed best, with higher precision than recall, and a 78.3% F-score.
SMAS Fusion Zones Determine the Subfascial and Subcutaneous Anatomy of the Human Face: Fascial Spaces, Fat Compartments, and Models of Facial Aging.

Fusion zones between superficial fascia and deep fascia have been recognized by surgical anatomists since 1938. Anatomical dissection performed by the author suggested that additional superficial fascia fusion zones exist.



A study was performed to evaluate and define fusion zones between the superficial and the deep fascia.



Dissection of fresh and minimally preserved cadavers was performed using the accepted technique for defining anatomic spaces: dye injection combined with cross-sectional anatomical dissection.



This study identified bilaminar membranes traveling from deep to superficial fascia at consistent locations in all specimens. These membranes exist as fusion zones between superficial and deep fascia, and are referred to as SMAS fusion zones.



Nerves, blood vessels and lymphatics transition between the deep and superficial fascia of the face by traveling along and within these membranes, a construct that provides stability and minimizes shear. Bilaminar subfascial membranes continue into the subcutaneous tissues as unilaminar septa on their way to skin. This three-dimensional lattice of interlocking horizontal, vertical, and oblique membranes defines the anatomic boundaries of the fascial spaces as well as the deep and superficial fat compartments of the face. This information facilitates accurate volume augmentation; helps to avoid facial nerve injury; and provides the conceptual basis for understanding jowls as a manifestation of enlargement of the buccal space that occurs with age.
Effective face frontalization in unconstrained images

“Frontalization” is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face recognition systems. This, by transforming the challenging problem of recognizing faces viewed from unconstrained viewpoints to the easier problem of recognizing faces in constrained, forward facing poses. Previous frontalization methods did this by attempting to approximate 3D facial shapes for each query image. We observe that 3D face shape estimation from unconstrained photos may be a harder problem than frontalization and can potentially introduce facial misalignments. Instead, we explore the simpler approach of using a single, unmodified, 3D surface as an approximation to the shape of all input faces. We show that this leads to a straightforward, efficient and easy to implement method for frontalization. More importantly, it produces aesthetic new frontal views and is surprisingly effective when used for face recognition and gender estimation.