nils-herrmann/clean_scirepeval huggingface dataset:
Quantifying Generalization in Reinforcement Learning

In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent’s ability to generalize. We address this issue by using procedurally generated environments to construct distinct training and test sets. Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL. Using CoinRun, we find that agents overfit to surprisingly large training sets. We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.
Automatic Tooth Segmentation of Dental Mesh Based on Harmonic Fields

An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.
Security Analysis of Contiki IoT Operating System

The Internet of Things (IoT) has introduced a myriad of ways in which devices can interact with each other. The IoT concept provides opportunities for novel and useful applications but at the same time, concerns have been raised over potential security issues caused by buggy IoT software. It is therefore imperative to detect and fix these bugs in order to minimise the risk of IoT devices becoming the target or source of attacks. In this paper, we focus our investigation on the underlying IoT operating system (OS), which is critical for the overall security of IoT devices. We picked Contiki as our case study since it is a very popular IoT OS and we have access to part of the development team, allowing us to discuss potential vulnerabilities with them so that fixes can be implemented quickly. Using static program analysis tools and techniques, we are able to scan the source code of the Contiki OS systematically in order to identify, analyse and patch vulnerabilities. Our main contribution is a holistic and systematic analysis of Contiki, starting with an exploration of its metrics, fundamental architecture, and finally some of its vulnerabilities. Our analysis produced relevant data on the number of unsafe functions in use, as well as the bug density; both of which provide an indication of the overall security of the inspected system. Our effort led to the finding of two major issues, described in two Common Vulnerabilities and Exposures (CVE) reports.
Are fast food restaurants an environmental risk factor for obesity?

Eating at "fast food" restaurants has increased and is linked to obesity. This study examined whether living or working near "fast food" restaurants is associated with body weight.



A telephone survey of 1033 Minnesota residents assessed body height and weight, frequency of eating at restaurants, and work and home addresses. Proximity of home and work to restaurants was assessed by Global Index System (GIS) methodology.



Eating at "fast food" restaurants was positively associated with having children, a high fat diet and Body Mass Index (BMI). It was negatively associated with vegetable consumption and physical activity. Proximity of "fast food" restaurants to home or work was not associated with eating at "fast food" restaurants or with BMI. Proximity of "non-fast food" restaurants was not associated with BMI, but was associated with frequency of eating at those restaurants.



Failure to find relationships between proximity to "fast food" restaurants and obesity may be due to methodological weaknesses, e.g. the operational definition of "fast food" or "proximity", or homogeneity of restaurant proximity. Alternatively, the proliferation of "fast food" restaurants may not be a strong unique cause of obesity.
A Statistical Model for Credit Scoring

We derive a model for consumer loan default and credit card expenditure. The default model is based on statistical models for discrete choice, in contrast to the usual procedure of linear discriminant analysis. The model is then extended to incorporate the default probability in a model of expected profit. The technique is applied to a large sample of applications and expenditure from a major credit card company. The nature of the data mandates the use of models of sample selection for estimation. The empirical model for expected profit produces an optimal acceptance rate for card applications which is far higher than the observed rate used by the credit card vendor based on the discriminant analysis. I am grateful to Terry Seaks for valuable comments on an earlier draft of this paper and to Jingbin Cao for his able research assistance. The provider of the data and support for this project has requested anonymity, so I must thank them as such. Their help and support are gratefully acknowledged. Participants in the applied econometrics workshop at New York University also provided useful commentary.
SHARVOT: Secret SHARe-Based VOTing on the Blockchain

Recently, there has been a growing interest in using online technologies to design protocols for secure electronic voting. The main challenges include vote privacy and anonymity, ballot irrevocability and transparency throughout the vote counting process. The introduction of the blockchain as a basis for cryptocurrency protocols, provides for the exploitation of the immutability and transparency properties of these distributed ledgers.
 In this paper, we discuss possible uses of the blockchain technology to implement a secure and fair voting system. In particular, we introduce a secret share-based voting system on the blockchain, the so-called SHARVOT protocol1. Our solution uses Shamir's Secret Sharing to enable on-chain, i.e. within the transactions script, votes submission and winning candidate determination. The protocol is also using a shuffling technique, Circle Shuffle, to de-link voters from their submissions.
Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.
Forecasting with artificial neural networks : The state of the art *

Interest in using artificial neural networks (ANNs) for forecasting has led to a tremendous surge in research activities in the past decade. While ANNs provide a great deal of promise, they also embody much uncertainty. Researchers to date are still not certain about the effect of key factors on forecasting performance of ANNs. This paper presents a state-of-the-art survey of ANN applications in forecasting. Our purpose is to provide (1) a synthesis of published research in this area, (2) insights on ANN modeling issues, and (3) the future research directions.
DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation

This paper investigates the piracy problem of deep learning models. Designing and training a wellperforming model is generally expensive. However, when releasing them, attackers may reverse engineer the models and pirate their design. This paper, therefore, proposes deep learning obfuscation, aiming at obstructing attackers from pirating a deep learning model. In particular, we focus on obfuscating convolutional neural networks (CNN), a widely employed type of deep learning architectures for image recognition. Our approach obfuscates a CNN model eventually by simulating its feature extractor with a shallow and sequential convolutional block. To this end, we employ a recursive simulation method and a joint training method to train the simulation network. The joint training method leverages both the intermediate knowledge generated by a feature extractor and data labels to train a simulation network. In this way, we can obtain an obfuscated model without accuracy loss. We have verified the feasibility of our approach with three prevalent CNNs, i.e., GoogLeNet, ResNet, and DenseNet. Although these networks are very deep with tens or hundreds of layers, we can simulate them in a shallow network including only five or seven convolutional layers. The obfuscated models are even more efficient than the original models. Our obfuscation approach is very effective to protect the critical structure of a deep learning model from being exposed to attackers. Moreover, it can also thwart attackers from pirating the model with transfer learning or incremental learning techniques because the shallow simulation network bears poor learning ability. To our best knowledge, this paper serves as a first attempt to obfuscate deep learning models, which may shed light on more future studies.
Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks

This work investigates the use of deep fully convolutional neural networks (DFCNN) for pixel-wise scene labeling of Earth Observation images. Especially, we train a variant of the SegNet architecture on remote sensing data over an urban area and study different strategies for performing accurate semantic segmentation. Our contributions are the following: 1) we transfer efficiently a DFCNN from generic everyday images to remote sensing images; 2) we introduce a multi-kernel convolutional layer for fast aggregation of predictions at multiple scales; 3) we perform data fusion from heterogeneous sensors (optical and laser) using residual correction. Our framework improves state-of-the-art accuracy on the ISPRS Vaihingen 2D Semantic Labeling dataset.
Grammatical Error Correction using Neural Networks

The growing number of English Language Learners has placed increased focus on developing automated resources to have a wider teaching impact. Prominent among those are the research efforts being undertaken in the field of automated Grammatical Error Correction. While rule-based approaches and machine translation models like phrasebased statistical machine translation models have so far been the popular choice in the field, there is significant interest to develop neural network models that can address the limitations posed by these models and improve results. Inspired by the progress achieved in this direction, this work presents an improved architecture of A Neural Network Global Lexicon Model which when added to a phrase-based Statistical Machine Translation model (SMT) improves the SMT baseline F0.5 score by 0.57.
Cultivating youth resilience to prevent bullying and cyberbullying victimization.

In an effort to better prevent and respond to bullying and cyberbullying, schools are recognizing a need to focus on positive youth development. One often-neglected developmental construct in this rubric is resilience, which can help students successfully respond to the variety of challenges they face. Enhancing this internal competency can complement the ever-present efforts of schools as they work to create a safe and supportive learning environment by shaping the external environment around the child. Based on a national sample of 1204 American youth between the ages of 12 and 17, we explore the relationship between resilience and experience with bullying and cyberbullying. We also examine whether resilient youth who were bullied (at school and online) were less likely to be significantly impacted at school. Results show resilience is a potent protective factor, both in preventing experience with bullying and mitigating its effect. Implications for school and community-based interventions are offered.
End-to-End Neural Ad-hoc Ranking with Kernel Pooling

This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score. The whole model is trained end-to-end. The ranking layer learns desired feature patterns from the pairwise ranking loss. The kernels transfer the feature patterns into soft-match targets at each similarity level and enforce them on the translation matrix. The word embeddings are tuned accordingly so that they can produce the desired soft matches. Experiments on a commercial search engine's query log demonstrate the improvements of K-NRM over prior feature-based and neural-based states-of-the-art, and explain the source of K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric tailored for matching query words to document words, and provides effective multi-level soft matches.
Beauty in a smile: the role of medial orbitofrontal cortex in facial attractiveness

The attractiveness of a face is a highly salient social signal, influencing mate choice and other social judgements. In this study, we used event-related functional magnetic resonance imaging (fMRI) to investigate brain regions that respond to attractive faces which manifested either a neutral or mildly happy face expression. Attractive faces produced activation of medial orbitofrontal cortex (OFC), a region involved in representing stimulus-reward value. Responses in this region were further enhanced by a smiling facial expression, suggesting that the reward value of an attractive face as indexed by medial OFC activity is modulated by a perceiver directed smile.
Meditation therapies for attention-deficit/hyperactivity disorder (ADHD).

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common developmental disorders experienced in childhood and can persist into adulthood. The disorder has early onset and is characterized by a combination of overactive, poorly modulated behavior with marked inattention. In the long term it can impair academic performance, vocational success and social-emotional development. Meditation is increasingly used for psychological conditions and could be used as a tool for attentional training in the ADHD population.



To assess the effectiveness of meditation therapies as a treatment for ADHD.


SEARCH STRATEGY
Our extensive search included: CENTRAL, MEDLINE, EMBASE, CINAHL, ERIC, PsycINFO, C2-SPECTR, dissertation abstracts, LILACS, Virtual Health Library (VHL) in BIREME, Complementary and Alternative Medicine specific databases, HSTAT, Informit, JST, Thai Psychiatric databases and ISI Proceedings, plus grey literature and trial registries from inception to January 2010.



Randomized controlled trials that investigated the efficacy of meditation therapy in children or adults diagnosed with ADHD.



Two authors extracted data independently using a pre-designed data extraction form. We contacted study authors for additional information required. We analyzed data using mean difference (MD) to calculate the treatment effect. The results are presented in tables, figures and narrative form.



Four studies, including 83 participants, are included in this review. Two studies used mantra meditation while the other two used yoga compared with drugs, relaxation training, non-specific exercises and standard treatment control. Design limitations caused high risk of bias across the studies. Only one out of four studies provided data appropriate for analysis. For this study there was no statistically significant difference between the meditation therapy group and the drug therapy group on the teacher rating ADHD scale (MD -2.72, 95% CI -8.49 to 3.05, 15 patients). Likewise, there was no statistically significant difference between the meditation therapy group and the standard therapy group on the teacher rating ADHD scale (MD -0.52, 95% CI -5.88 to 4.84, 17 patients). There was also no statistically significant difference between the meditation therapy group and the standard therapy group in the distraction test (MD -8.34, 95% CI -107.05 to 90.37, 17 patients).



As a result of the limited number of included studies, the small sample sizes and the high risk of bias, we are unable to draw any conclusions regarding the effectiveness of meditation therapy for ADHD. The adverse effects of meditation have not been reported. More trials are needed.
Securing balise-based train control systems using cryptographic random fountains

In modern train control systems, a moving train retrieves its location information through passive transponders called balises, which are placed on the sleepers of the track at regular intervals. When the train-borne antenna energizes them using tele-powering signals, balises backscatter preprogrammed telegrams, which carry information about the train's current location. Since the telegrams are static in the existing implementations, the uplink signals from the balises could be recorded by an adversary and then replayed at a different location of the track, leading to what is well-known as the replay attack. Such an attack, while the legitimate balise is still functional, introduces ambiguity to the train about its location, can impact the physical operations of the trains. For balise-to-train communication, we propose a new communication framework referred to as cryptographic random fountains (CRF), where each balise, instead of transmitting telegrams with fixed information, transmits telegrams containing random signals. A salient feature of CRF is the use of challenge-response based interaction between the train and the balise for communication integrity. We present a thorough security analysis of CRF to showcase its ability to mitigate sophisticated replay attacks. Finally, we also discuss the implementation aspects of our framework.
Unsupervised query segmentation using generative language models and wikipedia

In this paper, we propose a novel unsupervised approach to query segmentation, an important task in Web search. We use a generative query model to recover a query's underlying concepts that compose its original segmented form. The model's parameters are estimated using an expectation-maximization (EM) algorithm, optimizing the minimum description length objective function on a partial corpus that is specific to the query. To augment this unsupervised learning, we incorporate evidence from Wikipedia.
 Experiments show that our approach dramatically improves performance over the traditional approach that is based on mutual information, and produces comparable results with a supervised method. In particular, the basic generative language model contributes a 7.4% improvement over the mutual information based method (measured by segment F1 on the Intersection test set). EM optimization further improves the performance by 14.3%. Additional knowledge from Wikipedia provides another improvement of 24.3%, adding up to a total of 46% improvement (from 0.530 to 0.774).
New dynamic model for a Ballbot system

A Ballbot is a self-balanced mobile robot designed for omnidirectional mobility. The structure self-balanced on a ball giving to the system only one contact point with the ground. In this paper the dynamical model of a Ballbot system is investigated in order to find a linearized model which is able to describe the three-dimensional dynamics of the mechatronic system by a simpler set of equations. Due to the system's complexity, the equations of motion are often obtained by the energy method of Lagrange, they consist of a vast nonlinear ordinary differential equations (ODE), which are often numerically linearized for small perturbations. The present paper proposes to model the whole 3D dynamics of the Ballbot with the Newton-Euler formalism and Tait-Bryan angles in order to describe the model in terms of the system's physical parameters without resorting to numeric solution. This physical modelling is introduced to allow the simplification of the dynamic motion control of the ballbot.
Teaching Cyber Security through Competition An Experience Report about a Participatory Training Workshop

Training workshops and professional meetings are important tools for capacity building and professional development. These social events provide professionals and educators a platform where they can discuss and exchange constructive ideas, and receive feedback. In particular, competition-based training workshops where participants compete on solving similar and common challenging problems are effective tools for stimulating students’ learning and aspirations. This paper reports the results of a two-day training workshop where memory and disk forensics were taught using a competition-based security educational tool. The workshop included training sessions for professionals, educators, and students to learn features of Tracer FIRE, a competition-based digital forensics and assessment tool, developed by Sandia National Laboratories. The results indicate that competitionbased training can be very effective in stimulating students’ motivation to learn. However, extra caution should be taken into account when delivering these types of training workshops. Keywords-component; cyber security, digital forenciscs, partcipatory training workshop, competition-based learning,