nils-herrmann/clean_scirepeval huggingface dataset:
Generating 3D Faces Using Convolutional Mesh Autoencoders

Learned 3D representations of human faces are useful for computer vision problems such as 3D face tracking and reconstruction from images, as well as graphics applications such as character generation and animation. Traditional models learn a latent representation of a face using linear subspaces or higher-order tensor generalizations. Due to this linearity, they can not capture extreme deformations and nonlinear expressions. To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface. We introduce mesh sampling operations that enable a hierarchical mesh representation that captures non-linear variations in shape and expression at multiple scales within the model. In a variational setting, our model samples diverse realistic 3D faces from a multivariate Gaussian distribution. Our training data consists of 20,466 meshes of extreme expressions captured over 12 different subjects. Despite limited training data, our trained model outperforms state-of-the-art face models with 50% lower reconstruction error, while using 75% fewer parameters. We also show that, replacing the expression space of an existing state-ofthe-art face model with our autoencoder, achieves a lower reconstruction error. Our data, model and code are available at http://github.com/ anuragranj/coma.
Terrain Models for Mass Movement Erosion

In this paper we present a particle-based method for large scale long time progressive simulation of terrain erosion containing wet granular particles. The wetting process and the propagation through granular material is based on defining the wetness value for each particle representing the amount of water absorbed by granular particles and stored between them, as was originally proposed by Rungjiratananon [RSKN08]. We extend this model by adding a non homogeneous material to simulate differences between different types of soil-like granular material, based on physical constants like stability, plasticity and wetness. With this approach we can create a physical animation of erosion process like mass movement or mass wasting.
OctoMap : A Probabilistic , Flexible , and Compact 3 D Map Representation for Robotic Systems

In this paper, we present an approach for modeling 3D environments based on octrees using a probabilistic occupancy estimation. Our technique is able to represent full 3D models including free and unknown areas. It is available as an open-source library to facilitate the development of 3D mapping systems. We also provide a detailed review of existing approaches to 3D modeling. Our approach was thoroughly evaluated using different real-world and simulated datasets. The results demonstrate that our approach is able to model the data probabilistically while, at the same time, keeping the memory requirement at a minimum.
Intelligent fuzzy controller of a quadrotor

The aim of this work is to describe an intelligent system based on fuzzy logic that is developed to control a quadrotor. A quadrotor is a helicopter with four rotors, that make the vehicle more stable but more complex to model and to control. The quadrotor has been used as a testing platform in the last years for various universities and research centres. A quadrotor has six degrees of freedom, three of them regarding the position: height, horizontal and vertical motions; and the other three are related to the orientation: pitch, roll and yaw. A fuzzy control is designed and implemented to control a simulation model of the quadrotor. The inputs are the desired values of the height, roll, pitch and yaw. The outputs are the power of each of the four rotors that is necessary to reach the specifications. Simulation results prove the efficiency of this intelligent control strategy.
Analysis of Bayesian Classification based Approaches for Android Malware Detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, in this paper we develop and analyze proactive Machine Learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification based solutions for detecting unknown Android malware.
Expression of stressful experiences through writing: effects of a self-regulation manipulation for pessimists and optimists.

This study assessed the effectiveness of a writing task designed to foster self-regulatory coping with stressful experiences to reduce medical clinic visits and to promote adjustment. Students entering college (N = 122) who were classified as optimists or pessimists by using a dispositional optimism measure participated in a self-regulation task (expressing thoughts and feelings about entering college and then formulating coping plans), a disclosure task (expressing thoughts and feelings only), or a control task (writing about trivial topics) for 3 weekly writing sessions. Among optimists, both the self-regulation task and the disclosure task reduced illness-related clinic visits during the following month; among pessimists, only the self-regulation task reduced clinic visits. In general, the self-regulation task beneficially affected mood state and college adjustment whereas the disclosure task increased grade point averages.
Pricing models and Motivations for MMO play

The purpose of this study is to investigate and conceptualize the relationship between pricing models and motivations for MMO play. After the review of previous studies, we conduct an empirical research in the Japanese MMO industry to find determinants of pricing models. As a result, we found that (1) relationship between monthly fixed fee, continuous play (play period) and social motivation, (2) relationship between per-item billing, relatively short play period and high immersion.
CCR - A Content-Collaborative Reciprocal Recommender for Online Dating

We present a new recommender system for online dating. Using a large dataset from a major online dating website, we first show that similar people, as defined by a set of personal attributes, like and dislike similar people and are liked and disliked by similar people. This analysis provides the foundation for our Content-Collaborative Reciprocal (CCR) recommender approach. The content-based part uses selected user profile features and similarity measure to generate a set of similar users. The collaborative filtering part uses the interactions of the similar users, including the people they like/dislike and are liked/disliked by, to produce reciprocal recommendations. CCR addresses the cold start problem of new users joining the site by being able to provide recommendations immediately, based on their profiles. Evaluation results show that the success rate of the recommendations is 69.26% compared with a baseline of 35.19% for the top 10 ranked recommendations.
IT Security Governance: A Framework based on ISO 38500

ISO 38500 is an international standard for IT governance. The guidelines of ISO 38500 can also be applied at the IT security functional level in order to guide the governance of IT security. This paper proposes the use of a strategic information security management (ISM) framework to implement guidelines of ISO 38500. This approach provides several strategic advantages to the organization by 1) aligning IT security initiatives to business strategy; 2) providing a mechanism for establishing and tracking security metrics; and 3) enhancing the overall maturity of business, IT and IT security processes. The framework also leverages tools such as COBIT, the Balanced Scorecard and SSE-CMM in order to implement IT security governance and continuous improvement practices. Using extant literature, this paper identifies certain challenges and solutions with respect to the governance of IT security. For practitioners, it highlights relevant links between principles of ISO 38500 and IT governance, provides an over-arching contextual framework to drive IT security governance, and demonstrates mitigation solutions for IT security governance challenges. For academics, the paper makes theoretical contributions, by relating IT security governance to business strategy and proposing that firms develop dynamic governance capabilities (Pavlou and El Sawy, 2010) or organizational learning ladders (Ciborra and Andreu, 2010).
Zara The Supergirl: An Empathetic Personality Recognition System

Zara the Supergirl is an interactive system that, while having a conversation with a user, uses its built in sentiment analysis, emotion recognition, facial and speech recognition modules, to exhibit the human-like response of sharing emotions. In addition, at the end of a 5-10 minute conversation with the user, it can give a comprehensive personality analysis based on the user’s interaction with Zara. This is a first prototype that has incorporated a full empathy module, the recognition and response of human emotions, into a spoken language interactive system that enhances human-robot understanding. Zara was shown at the World Economic Forum in Dalian in September 2015.
Soft Robot Arm Inspired by the Octopus

The octopus is a marine animal whose body has no rigid structures. It has eight arms composed of a peculiar muscular structure, named a muscular hydrostat. The octopus arms provide it with both locomotion and grasping capabilities, thanks to the fact that their stiffness can change over a wide range and can be controlled through combined contractions of the muscles. The muscular hydrostat can better be seen as a modifiable skeleton. Furthermore, the morphology the arms and the mechanical characteristics of their tissues are such that the interaction with the environment (i.e., water) is exploited to simplify control. Thanks to this effective mechanism of embodied intelligence, the octopus can control a very high number of degrees of freedom, with relatively limited computing resources. From these considerations, the octopus emerges as a good model for embodied intelligence and for soft robotics. The prototype of a robot arm has been built based on an artificial muscular hydrostat inspired to the muscular hydrostat of the Octopus vulgaris. The prototype presents the morphology of the biological model and the broad arrangement of longitudinal and transverse muscles. Actuation is obtained with cables (longitudinally) and with shape memory alloy springs (transversally). The robot arm combines contractions and it can show the basic movements of the octopus arm, like elongation, shortening and bending, in water.
GLCM Textural Features for Brain Tumor Classification

Automatic recognition system for medical images is challenging task in the field of medical image processing. Medical images acquired from different modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), etc which are used for the diagnosis purpose. In the medical field, brain tumor classification is very important phase for the further treatment. Human interpretation of large number of MRI slices (Normal or Abnormal) may leads to misclassification hence there is need of such a automated recognition system, which can classify the type of the brain tumor. In this research work, we used four different classes of brain tumors and extracted the GLCM based textural features of each class, and applied to twolayered Feed forward Neural Network, which gives 97.5% classification rate.
An analysis of shared leadership, diversity, and team creativity in an e-learning environment

In response to rapid change and fierce competition, creativity is an imperative factor to develop and implement innovation. Hence, most firms have pursued diverse strategies to promote individual and team creativity in the workplace. Shared leadership is a voluntarily, informally emergent structure beyond vertical leadership. A team is composed of individual members, and shared leadership and demographic diversity exist within the team, influencing team creativity. In this respect, we introduced shared leadership as a social network perspective as well as diversity into a team creativity model. In sum, we examined the influence of shared leadership and diversity on knowledge sharing and the subsequent effects on team creativity. Our results showed that role diversity directly influences team creativity, with shared leadership and knowledge sharing positively contributing to team creativity. Thus, knowledge sharing had a partially mediating role between shared leadership and team creativity. Apart from our hypotheses, the present results implied that if gender diversity (as a differentiated factor) is not a minority status, knowledge sharing may have a fully mediating effect between gender diversity and team creativity.
A new weighted SVDD algorithm for outlier detection

Traditional Support Vector Domain Description (SVDD) and some improved SVDD algorithms are suboptimal in detecting some complex outliers. To solve the difficulty, we proposed a novel adaptive weighted SVDD (AW-SVDD) algorithm. Firstly, a weighting is computed for each data point based on their spatial position distribution and density distribution in a training dataset. The weighting can be used to measure the degree of the data point to be an outlier. Then the weighted data are trained by traditional SVDD. Lastly, a sphere shaped data description can be obtained for the training data. Experimental results demonstrate that AW-SVDD can overcome the interference from some complex outliers objectively, therefore the algorithm has a better performance than traditional SVDD and other improved SVDD algorithms.
Towards an Information Security Framework for the Automotive Domain

Driven by increasing connectivity, complexity, and distribution of functions in vehicles, automotive security is steadily gaining attentio n. While in other domains there is a harmonized notion on security requirements a nd here are even broad information security management standards, in the auto motive domain there is so far no common structured approach to achieve system secu rity. At the same time, the automotive domain shows some special properties that dif ferentiate it from other domains and prevent direct application of available approac hes. In this work, we take a step towards an automotive information secu rity framework. We provide an overview of existing system security an d security development standards from related domains and provide motivation for harmonization on one hand and a sector-specific standard on the ther. We outline core ideas and elements of a possible framework for automotive sec urity engineering, illustrate them with examples, and put them in context with existing related information security standards.
Control of contact via tactile sensing

In this paper, we present our approach to using tactile sensing in the feedback control of robot contact tasks. A general framework, called tactile servo, is first introduced. We then address the critical issue of how to model the state of contact in terms that are both sufficient for defining general contacts and conducive to bridging the gap between a robot task description and the information observable by a tactile sensor. We subsequently examine techniques for deriving tactile sensor models that are required for computing the state of contact from the sensor output. Two basic methods for mapping a tactile sensor image to the contact state variables are introduced—one based on explicit inverse modeling and the other on numerical tactile Jacobian. In both cases, moments of the tactile sensor image are used as the features that capture the necessary information about contact. The theoretical development is supported by extensive experiments, which include edge tracking, object shape construction, and object manipulation.
A fast and efficient python library for interfacing with the Biological Magnetic Resonance Data Bank

The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis. To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files.  User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR. The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.
Firmware modification attacks on programmable logic controllers

Recent attacks on industrial control systems, such as the highly publicized Stuxnet malware, have intensified a “race to the bottom” where lower-level attacks have a tactical advantage. Programmable logic controller (PLC) firmware, which provides a softwaredriven interface between system inputs and physical outputs, can be easily modified at the user level. Efforts directed at protecting against firmware modification are hindered by the lack of foundational research about attack development and implementation. This paper examines the vulnerability of PLCs to intentional firmware modifications in order to obtain a better understanding of the threats posed by PLC firmware modification attacks and the feasibility of these attacks. A general firmware analysis methodology is presented, and a proof-of-concept experiment is used to demonstrate how legitimate firmware can be updated and uploaded to an Allen-Bradley ControlLogix L61 PLC.
BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory

Storing a database (rows and indexes) entirely in non-volatile memory (NVM) potentially enables both high performance and fast recovery. To fully exploit parallelism on modern CPUs, modern main-memory databases use latch-free (lock-free) index structures, e.g. Bw-tree or skip lists. To achieve high performance NVMresident indexes also need to be latch-free. This paper describes the design of the BzTree, a latch-free B-tree index designed for NVM. The BzTree uses a persistent multi-word compare-and-swap operation (PMwCAS) as a core building block, enabling an index design that has several important advantages compared with competing index structures such as the Bw-tree. First, the BzTree is latch-free yet simple to implement. Second, the BzTree is fast showing up to 2x higher throughput than the Bw-tree in our experiments. Third, the BzTree does not require any special-purpose recovery code. Recovery is near-instantaneous and only involves rolling back (or forward) any PMwCAS operations that were in-flight during failure. Our end-to-end recovery experiments of BzTree report an average recovery time of 145 μs. Finally, the same BzTree implementation runs seamlessly on both volatile RAM and NVM, which greatly reduces the cost of code maintenance. PVLDB Reference Format: Joy Arulraj, Justin Levandoski, Umar Farooq Minhas,Per-Ake Larson.  A High-Performance Latch-free Range Index for Non-Volatile Memory. PVLDB, 11(4): xxxx-yyyy, 2018.
From Properties to Links: Deep Network Embedding on Incomplete Graphs

As an effective way of learning node representations in networks, network embedding has attracted increasing research interests recently. Most existing approaches use shallow models and only work on static networks by extracting local or global topology information of each node as the algorithm input. It is challenging for such approaches to learn a desirable node representation on incomplete graphs with a large number of missing links or on dynamic graphs with new nodes joining in. It is even challenging for them to deeply fuse other types of data such as node properties into the learning process to help better represent the nodes with insufficient links. In this paper, we for the first time study the problem of network embedding on incomplete networks. We propose a Multi-View Correlation-learning based Deep Network Embedding method named MVC-DNE to incorporate both the network structure and the node properties for more effectively and efficiently perform network embedding on incomplete networks. Specifically, we consider the topology structure of the network and the node properties as two correlated views. The insight is that the learned representation vector of a node should reflect its characteristics in both views. Under a multi-view correlation learning based deep autoencoder framework, the structure view and property view embeddings are integrated and mutually reinforced through both self-view and cross-view learning. As MVC-DNE can learn a representation mapping function, it can directly generate the representation vectors for the new nodes without retraining the model. Thus it is especially more efficient than previous methods. Empirically, we evaluate MVC-DNE over three real network datasets on two data mining applications, and the results demonstrate that MVC-DNE significantly outperforms state-of-the-art methods.