nils-herrmann/clean_scirepeval huggingface dataset:
Privacy on Reddit? Towards Large-scale User Classification

Reddit is a social news website that aims to provide user privacy by encouraging them to use pseudonyms and refraining from any kind of personal data collection. However, users are often not aware of possibilities to indirectly gather a lot of information about them by analyzing their contributions and behaviour on this site. In order to investigate the feasibility of large-scale user classification with respect to the attributes social gender and citizenship this article provides and evaluates several data mining techniques. First, a large text corpus is collected from Reddit and annotations are derived using lexical rules. Then, a discriminative approach on classification using support vector machines is undertaken and extended by using topics generated by a latent Dirichlet allocation as features. Based on supervised latent Dirichlet allocation, a new generative model is drafted and implemented that captures Reddit’s specific structure of organizing information exchange. Finally, the presented techniques for user classification are evaluated and compared in terms of classification performance as well as time efficiency. Our results indicate that large-scale user classification on Reddit is feasible, which may raise privacy concerns among its community.
Embodied Artificial Intelligence

The field of Artificial Intelligence, which started roughly half a century ago, has a turbulent history. In the 1980s there has been a major paradigm shift towards embodiment. While embodied artificial intelligence is still highly diverse, changing, and far from “theoretically stable”, a certain consensus about the important issues and methods has been achieved or is rapidly emerging. In this non-technical paper we briefly characterize the field, summarize its achievements, and identify important issues for future research. One of the fundamental unresolved problems has been and still is how thinking emerges from an embodied system. Provocatively speaking, the central issue could be captured by the question “How does walking relate to thinking?”
BGPR_Reconstruct: A MATLAB® ray-tracing program for nonlinear inversion of first arrival travel time data from zero-offset borehole radar

A MATLAB program was developed to invert first arrival travel time picks from zero offset profiling borehole ground penetrating radar traces to obtain the electromagnetic wave propagation velocities in soil. Zero-offset profiling refers to a mode of operation wherein the centers of the bistatic antennae being lowered to the same depth below ground for each measurement. The inversion uses a simulated annealing optimization routine, whereby the model attempts to reduce the root mean square error between the measured and modeled travel time by perturbing the velocity in a ray tracing routine. Measurement uncertainty is incorporated through the presentation of the ensemble mean and standard deviation from the results of a Monte Carlo simulation. The program features a pre-processor to modify or delete travel time information from the profile before inversion and post-processing through presentation of the ensemble statistics of the water contents inferred from the velocity profile. The program includes a novel application of a graphical user interface to animate the velocity fitting routine.
No . 786 DECEPTION AND CONFESSION : EXPERIMENTAL EVIDENCE FROM A DECEPTION GAME IN JAPAN

This study investigated lying behavior and the behavior of people who are deceived by using a deception game (Gneezy, 2005) in both anonymity and face-to-face treatments. Subjects consist of students and non-students (citizens) to investigate whether lying behavior is depended on socioeconomic backgrounds. To explore how liars feel about lying, we give senders a chance to confess their behaviors to their counter partner for the guilty aversion of lying. The following results are obtained: i) a frequency of lying behavior for students is significantly higher than that for non-students at a payoff in the anonymity treatment, but that is not significantly difference between the anonymity and face-to-face treatments; ii) lying behavior is not influenced by gender; iii) a frequency of confession is higher in the face-to-face treatment than in the anonymity treatment; and iv) the receivers who are deceived are more likely to believe a sender’s message to be true in the anonymity treatment. This study implies that the existence of the partner prompts liars to confess their behavior because they may feel remorse or guilt.
Continual Reinforcement Learning with Complex Synapses

Unlike humans, who are capable of continual learning over their lifetimes, artificial neural networks have long been known to suffer from a phenomenon known as catastrophic forgetting, whereby new learning can lead to abrupt erasure of previously acquired knowledge. Whereas in a neural network the parameters are typically modelled as scalar values, an individual synapse in the brain comprises a complex network of interacting biochemical components that evolve at different timescales. In this paper, we show that by equipping tabular and deep reinforcement learning agents with a synaptic model that incorporates this biological complexity (Benna & Fusi, 2016), catastrophic forgetting can be mitigated at multiple timescales. In particular, we find that as well as enabling continual learning across sequential training of two simple tasks, it can also be used to overcome within-task forgetting by reducing the need for an experience replay database.
6D hands: markerless hand-tracking for computer aided design

Aided Design (CAD) typically involves tasks such as adjusting the camera perspective and assembling pieces in free space that require specifying 6 degrees of freedom (DOF). The standard approach is to factor these DOFs into 2D subspaces that are mapped to the x and y axes of a mouse. This metaphor is inherently modal because one needs to switch between subspaces, and disconnects the input space from the modeling space. In this paper, we propose a bimanual hand tracking system that provides physically-motivated 6-DOF control for 3D assembly. First, we discuss a set of principles that guide the design of our precise, easy-to-use, and comfortable-to-use system. Based on these guidelines, we describe a 3D input metaphor that supports constraint specification classically used in CAD software, is based on only a few simple gestures, lets users rest their elbows on their desk, and works alongside the keyboard and mouse. Our approach uses two consumer-grade webcams to observe the user's hands. We solve the pose estimation problem with efficient queries of a precomputed database that relates hand silhouettes to their 3D configuration. We demonstrate efficient 3D mechanical assembly of several CAD models using our hand-tracking system.
Radio transformer networks: Attention models for learning to synchronize in wireless systems

We introduce learned attention models into the radio machine learning domain for the task of modulation recognition by leveraging spatial transformer networks and introducing new radio domain appropriate transformations. This attention model allows the network to learn a localization network capable of synchronizing and normalizing a radio signal blindly with zero knowledge of the signal's structure based on optimization of the network for classification accuracy, sparse representation, and regularization. Using this architecture we are able to outperform our prior results in accuracy vs signal to noise ratio against an identical system without attention, however we believe such an attention model has implication far beyond the task of modulation recognition.
Data preprocessing for anomaly based network intrusion detection: A review

Data preprocessing is widely recognized as an important stage in anomaly detection. This paper reviews the data preprocessing techniques used by anomaly-based network intrusion detection systems (NIDS), concentrating on which aspects of the network traffic are analyzed, and what feature construction and selection methods have been used. Motivation for the paper comes from the large impact data preprocessing has on the accuracy and capability of anomaly-based NIDS. The review finds that many NIDS limit their view of network traffic to the TCP/IP packet headers. Time-based statistics can be derived from these headers to detect network scans, network worm behavior, and denial of service attacks. A number of other NIDS perform deeper inspection of request packets to detect attacks against network services and network applications. More recent approaches analyze full service responses to detect attacks targeting clients. The review covers a wide range of NIDS, highlighting which classes of attack are detectable by each of these approaches. Data preprocessing is found to predominantly rely on expert domain knowledge for identifying the most relevant parts of network traffic and for constructing the initial candidate set of traffic features. On the other hand, automated methods have been widely used for feature extraction to reduce data dimensionality, and feature selection to find the most relevant subset of features from this candidate set. The review shows a trend towards deeper packet inspection to construct more relevant features through targeted content parsing. These context sensitive features are required to detect current attacks.
A Compact Embedding for Facial Expression Similarity

Most of the existing work on automatic facial expression analysis focuses on discrete emotion recognition, or facial action unit detection. However, facial expressions do not always fall neatly into pre-defined semantic categories. Also, the similarity between expressions measured in the action unit space need not correspond to how humans perceive expression similarity. Different from previous work, our goal is to describe facial expressions in a continuous fashion using a compact embedding space that mimics human visual preferences. To achieve this goal, we collect a large-scale faces-in-the-wild dataset with human annotations in the form: Expressions A and B are visually more similar when compared to expression C, and use this dataset to train a neural network that produces a compact (16-dimensional) expression embedding. We experimentally demonstrate that the learned embedding can be successfully used for various applications such as expression retrieval, photo album summarization, and emotion recognition. We also show that the embedding learned using the proposed dataset performs better than several other embeddings learned using existing emotion or action unit datasets.
Perpendicular Magnetic Anisotropy and Easy Cone State in Ta/Co60Fe20B20 /MgO

We used broadband ferromagnetic resonance (FMR) spectroscopy to measure the second- and fourth-order perpendicular magnetic anisotropies in Ta/(t) Co<sub>60</sub>Fe<sub>20</sub>B<sub>20</sub>/MgO layers over a Co<sub>60</sub>Fe<sub>20</sub>B<sub>20</sub> thickness range of 5.0 nm ≥ t ≥ 0.8 nm. Fort > 1.0 nm, the easy axis is in the plane of the film, but when t <; 1.0 nm, the easy axis is directed perpendicular to the surface. However, the presence of a substantial higher order perpendicular anisotropy results in an easy cone state when t = 1.0 nm. Angular-dependent FMR measurements verify the presence of the easy cone state. Measurement of the spectroscopic g-factor via FMR for both the in-plane and out-of-plane geometries suggests a significant change in electronic and/or physical structure at t ≈ 1.0 nm thickness.
Spider Venoms

Spiders occupy most of the ecological niches of the planet, revealing a huge adaptive plasticity, reflected in the chemical diversity of their venom toxins. The spiders are distributed throughout the planet, adapting themselves to many P.C. Gomes Department of Biology/CEIS/Institute of Biosciences of Rio Claro, University of São Paulo State (UNESP), Rio Claro, SP, Brazil e-mail: pccesar@rc.unesp.br M.S. Palma (*) Department of Biology, CEIS, Laboratory of Structural Biology and Zoochemistry, Sao Paulo, State University (UNESP), Institute of Biosciences, Rio Claro, SP, Brazil e-mail: mspalma@rc.unesp.br # Springer Science+Business Media Dordrecht 2016 P. Gopalakrishnakone et al. (eds.), Spider Venoms, Toxinology, DOI 10.1007/978-94-007-6389-0_14 3 different environments, to form the largest taxonomic group of organisms with a diet exclusively carnivorous. The organic low-molecular-mass compounds present in spider venoms are used both for defensive purposes and to paralyze/kill their preys. Among the low-molecular-mass organic compounds present in spider venoms, the most common ones are free organic acids, amino acids, biogenic amines, and neurotransmitters. These compounds were also used in the course of evolution as substrates for the biosynthesis of novel spider toxins, which were neglected by the toxinology during a long time, mainly due to the difficulties to isolate and to assign the chemical structures of very low abundant compounds. However, the recent technological advances in the spectroscopic techniques used for structural analysis of small molecules allowed the structural elucidation of many of these toxins in spider venoms, permitting the identification of at least six families of low-molecular-mass toxins in spider venoms: (i) acylpolyamines, (ii) nucleoside analogs, (iii) bis(agmatine)oxalamide, (iv) the betacarboline alkaloids, (v) organometallic diazenaryl compounds, and (vi) dioxopiperidinic analogs. Investigations of structure/activity relationship of these toxins revealed that some of them have been identified both as interesting tools for chemical investigations in neurobiology and as potential models for the rational development of novel drugs for neurotherapeutic uses, as well as for developing specific insecticides. List of Abbreviations 13C Carbon-13 1H Hydrogen-1 ALS Amyotrophic lateral sclerosis AMPA α-Amino-3-hydroxy-5-methylisoxasole-4-propionic acid CID Collisional-induced dissociation CNS Central nervous systems COSY Homonuclear correlation spectroscopy dqf COSY Double-quantum-filter-COSY ESI-MS Electron spray ionization mass spectrometry FRIT-FAB Continuous-flow fast atom bombardment FTX Funnel web toxin GABA Gamma-aminobutyric acid Glu-R Glutamate receptor HMBC Heteronuclear multiple bond coherence HMQC Heteronuclear multiple quantum coherence HPLC High-performance liquid chromatography HRMS High-resolution mass spectrometry JSTX Joro spider toxin KA Kainic acid kDa Kilodalton L-Arg-3,4 L-Arginyl-3,4-spermidine LC-MS Liquid chromatography mass spectrometry LMM Low molecular mass 4 P.C. Gomes and M.S. Palma
Authorship Attribution and Verification with Many Authors and Limited Data

Most studies in statistical or machine learning based authorship attribution focus on two or a few authors. This leads to an overestimation of the importance of the features extracted from the training data and found to be discriminating for these small sets of authors. Most studies also use sizes of training data that are unrealistic for situations in which stylometry is applied (e.g., forensics), and thereby overestimate the accuracy of their approach in these situations. A more realistic interpretation of the task is as an authorship verification problem that we approximate by pooling data from many different authors as negative examples. In this paper, we show, on the basis of a new corpus with 145 authors, what the effect is of many authors on feature selection and learning, and show robustness of a memory-based learning approach in doing authorship attribution and verification with many authors and limited training data when compared to eager learning methods such as SVMs and maximum entropy learning.
Agent-based decision-making process in airport ground handling management

Recent research on highly distributed control methods has produced a series of new philosophies based on negotiation, which bring together the process engineering with computer science. Among these control philosophies, the ones based on Multi-agent Systems (MAS) have become especially relevant to address complex tasks and to support distributed decision making in asset management, manufacturing, and logistics. However, these MAS models have the drawback of an excessive dependence on up-to-date field information. In this work, a theoretical and experimental MAS, called MAS-DUO, is presented to test new strategies for managing handling operations supported by feedback coming from radio frequency identification (RFID) systems. These strategies have been based on a new distributed organization model to enforce the idea of division between physical elements and information and communication technologies (ICT) in the product scheduling control. This division in two platforms simplifies the design, the development, and the validation of the MAS, allowing an abstraction and preserving the independency between platforms. The communication between both platforms is based on sharing the parameters of the Markov reward function. This function is mainly made up of the field information coming from the RFID readers incorporated as the internal beliefs of the agent. The proposed MAS have been deployed on the Ciudad Real Central Airport in Spain in order to dimension the ground handling resources.
Bayesian Additive Regression Trees-Based Spam Detection for Enhanced Email Privacy

Spam is considered an invasion of privacy. Its changeable structures and variability raise the need for new spam classification techniques. The present study proposes using Bayesian additive regression trees (BART) for spam classification and evaluates its performance against other classification methods, including logistic regression, support vector machines, classification and regression trees, neural networks, random forests, and naive Bayes. BART in its original form is not designed for such problems, hence we modify BART and make it applicable to classification problems. We evaluate the classifiers using three spam datasets; Ling-Spam, PU1, and Spambase to determine the predictive accuracy and the false positive rate.
Experience of Transfistula (TFARP) Repair for Congenital Recto-Vestibular Fistula

The objective of this study is to assess the feasibility of aprimary transfistula anorectoplasty (TFARP) in congenital recto-vestibular fistula without a covering colostomy in the north of Iraq.  Female patients having imperforate anus with congenital rectovestibular fistula presenting to pediatric surgical centres in the north of Iraq (Mosul & Erbil) between 1995 to 2011 were reviewed in a nonrandomized manner, after excluding those with pouch colon, rectovaginal fistula and patients with colostomy. All cases underwent one stage primary (TFARP) anorectoplasty at age between 1-30 months, after on table rectal irrigation with normal saline & povidoneIodine. They were kept nil by mouth until 24 hours postoperatively. Postoperative regular anal dilatation were commenced after 2 weeks of operation when needed. The results were evaluated for need of bowel preparation, duration of surgery,, cosmetic appearance, commencement of feed and hospital stay,postoperative results. Patients were also followed up for assessment of continence and anal dilatation.
Simulation optimization: a review of algorithms and applications

Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation—discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise—various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.
Bag-of-Words Image Representation: Key Ideas and Further Insight

In the context of object and scene recognition, state-of-the-art performances are obtained with visual Bag-of-Words (BoW) models of mid-level representations computed from dense sampled local descriptors (e.g., Scale-Invariant Feature Transform (SIFT)). Several methods to combine low-level features and to set mid-level parameters have been evaluated recently for image classification. In this chapter, we study in detail the different components of the BoW model in the context of image classification. Particularly, we focus on the coding and pooling steps and investigate the impact of the main parameters of the BoW pipeline. We show that an adequate combination of several low (sampling rate, multiscale) and mid-level (codebook size, normalization) parameters is decisive to reach good performances. Based on this analysis, we propose a merging scheme that exploits the specificities of edge-based descriptors. Low and high contrast regions are pooled separately and combined to provide a powerful representation of images. We study the impact on classification performance of the contrast threshold that determines whether a SIFT descriptor corresponds to a low contrast region or a high contrast region. Successful experiments are provided on the Caltech-101 and Scene-15 datasets. M. T. Law (B) · N. Thome · M. Cord LIP6, UPMC—Sorbonne University, Paris, France e-mail: Marc.Law@lip6.fr N. Thome e-mail: Nicolas.Thome@lip6.fr M. Cord e-mail: Matthieu.Cord@lip6.fr B. Ionescu et al. (eds.), Fusion in Computer Vision, Advances in Computer 29 Vision and Pattern Recognition, DOI: 10.1007/978-3-319-05696-8_2,
Superficial dorsal penile vein thrombosis: a little-known complication of subinguinal varicocelectomy.

To describe the symptomatology, diagnosis and treatment of superficial thrombosis of the dorsal penile vein - the most common complication of subinguinal varicocelectomy - and analyse the possible mechanisms involved in the development of the condition.



The clinical records of 326 patients who underwent varicocele repair during the last 10 years was reviewed. The technique used was subinguinal varicocelectomy with arterial preservation. A mini-Doppler probe was used during surgery for artery identification. We report on the postoperative complications of varicocelectomy, with special attention to superficial dorsal penile vein thrombosis, and provide a detailed description of the anatomy of the superficial venous system of the penis.



Complications usually associated with varicocele surgery occurred in less than 1% of patients. However, the most common complication in our series was superficial dorsal penile vein thrombosis, which occurred in 2.1% of patients. The use of the mini-Doppler probe allowed us to identify and preserve the arteries in all 326 patients.



Subinguinal varicocelectomy with intra-operative use of a mini-Doppler probe is a rapid and safe technique. The outcomes and complications are similar to those reported for subinguinal microscopic varicocelectomy. Superficial dorsal penile vein thrombosis is a benign self-limited condition whose association with subinguinal varicocelectomy has not been previously reported.