nils-herrmann/clean_scirepeval huggingface dataset:
Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications

This paper presents an online highly accurate system for automatic number plate recognition (ANPR) that can be used as a basis for many real-world ITS applications. The system is designed to deal with unclear vehicle plates, variations in weather and lighting conditions, different traffic situations, and high-speed vehicles. This paper addresses various issues by presenting proper hardware platforms along with real-time, robust, and innovative algorithms. We have collected huge and highly inclusive data sets of Persian license plates for evaluations, comparisons, and improvement of various involved algorithms. The data sets include images that were captured from crossroads, streets, and highways, in day and night, various weather conditions, and different plate clarities. Over these data sets, our system achieves 98.7%, 99.2%, and 97.6% accuracies for plate detection, character segmentation, and plate recognition, respectively. The false alarm rate in plate detection is less than 0.5%. The overall accuracy on the dirty plates portion of our data sets is 91.4%. Our ANPR system has been installed in several locations and has been tested extensively for more than a year. The proposed algorithms for each part of the system are highly robust to lighting changes, size variations, plate clarity, and plate skewness. The system is also independent of the number of plates in captured images. This system has been also tested on three other Iranian data sets and has achieved 100% accuracy in both detection and recognition parts. To show that our ANPR is not language dependent, we have tested our system on available English plates data set and achieved 97% overall accuracy.
Batch-Mode Active Learning via Error Bound Minimization

Active learning has been proven to be quite effective in reducing the human labeling efforts by actively selecting the most informative examples to label. In this paper, we present a batch-mode active learning method based on logistic regression. Our key motivation is an out-of-sample bound on the estimation error of class distribution in logistic regression conditioned on any fixed training sample. It is different from a typical PACstyle passive learning error bound, that relies on the i.i.d. assumption of example-label pairs. In addition, it does not contain the class labels of the training sample. Therefore, it can be immediately used to design an active learning algorithm by minimizing this bound iteratively. We also discuss the connections between the proposed method and some existing active learning approaches. Experiments on benchmark UCI datasets and text datasets demonstrate that the proposed method outperforms the state-of-the-art active learning methods significantly.
Illustrative Language Understanding: Large-Scale Visual Grounding with Image Search

We introduce Picturebook, a large-scale lookup operation to ground language via ‘snapshots’ of our physical world accessed through image search. For each word in a vocabulary, we extract the top-k images from Google image search and feed the images through a convolutional network to extract a word embedding. We introduce a multimodal gating function to fuse our Picturebook embeddings with other word representations. We also introduce Inverse Picturebook, a mechanism to map a Picturebook embedding back into words. We experiment and report results across a wide range of tasks: word similarity, natural language inference, semantic relatedness, sentiment/topic classification, image-sentence ranking and machine translation. We also show that gate activations corresponding to Picturebook embeddings are highly correlated to human judgments of concreteness ratings.
A dual-metal-trench Schottky pinch-rectifier in 4H-SiC

Characteristics of high-voltage dual-metal-trench (DMT) SiC Schottky pinch-rectifiers are reported for the first time. At a reverse bias of 300 V, the reverse leakage current of the SiC DMT device is 75 times less than that of a planar device while the forward bias characteristics remain comparable to those of a planar device. In this work, 4H-SiC pinch-rectifiers have been fabricated using a small/large barrier height (Ti/Ni) DMT device structure. The DMT structure is specially designed to permit simple fabrication in SiC. The Ti Schottky contact metal serves as a self-aligned trench etch mask and only four basic fabrication steps are required.
The social side of gaming: How playing online computer games creates online and offline social support

0747-5632/$ see front matter 2012  A doi:10.1016/j.chb.2011.12.003 ⇑ Corresponding author.  fax E-mail addresses: sabine.trepte@uni-hamburg.de ( uni-hamburg.de (L. Reinecke), keno.juechems@stu Juechems). Online gaming has gained millions of users around the globe, which have been shown to virtually connect, to befriend, and to accumulate online social capital. Today, as online gaming has become a major leisure time activity, it seems worthwhile asking for the underlying factors of online social capital acquisition and whether online social capital increases offline social support. In the present study, we proposed that the online game players’ physical and social proximity as well as their mutual familiarity influence bridging and bonding social capital. Physical proximity was predicted to positively influence bonding social capital online. Social proximity and familiarity were hypothesized to foster both online bridging and bonding social capital. Additionally, we hypothesized that both social capital dimensions are positively related to offline social support. The hypotheses were tested with regard to members of e-sports clans. In an online survey, participants (N = 811) were recruited via the online portal of the Electronic Sports League (ESL) in several countries. The data confirmed all hypotheses, with the path model exhibiting an excellent fit. The results complement existing research by showing that online gaming may result in strong social ties, if gamers engage in online activities that continue beyond the game and extend these with offline activities.
New security features in DLMS/COSEM — A comparison to the smart meter gateway

In this paper we describe the new security features of the international standard DLMS/COSEM that came along with its new Green Book Ed. 8. We compare them with those of the German Smart Meter Gateway approach that uses TLS to protect the privacy of connections. We show that the security levels of the cryptographic core methods are similar in both systems. However, there are several aspects concerning the security on which the German approach provides more concrete implementation instructions than DLMS/COSEM does (like lifetimes of certificates and random generators). We describe the differences in security and architecture of the two systems.
MACCEPA 2.0: Adjustable compliant actuator with stiffening characteristic for energy efficient hopping

The MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator) is an electric actuator of which the compliance and equilibrium position are fully independently controllable and both are set by a dedicated servomotor. In this paper an improvement of the actuator is proposed where the torque-angle curve and consequently the stiffness-angle curve can be modified by choosing an appropriate shape of a profile disk, which replaces the lever arm of the former design. The actuator has a large joint angle, torque and stiffness range and these properties can be made beneficial for safe human robot interaction and the construction of energy efficient walking, hopping and running robots. The ability to store and release energy is shown by simulations on a 1DOF hopping robot. Its hopping height is much higher compared to a configuration in which the same motor is used in a traditional stiff setup. The stiffness of the actuator has a stiffening characteristic so the leg stiffness resembles more a linear stiffness as found in humans.
A Survey on Density Based Clustering Algorithms for Mining Large Spatial Databases

Density based clustering algorithm is one of the primary methods for clustering in data mining. The clusters which are formed based on the density are easy to understand and it does not limit itself to the shapes of clusters. This paper gives a detailed survey of the existing density based algorithms namely DBSCAN, VDBSCAN, DVBSCAN, ST-DBSCAN and DBCLASD based on the essential parameters needed for a good clustering algorithm. We analyse the algorithms in terms of the parameters essential for creating meaningful clusters.
Adapting to a Market Shock: Optimal Sequential Market-Making

We study the profit-maximization problem of a monopolistic market-maker. The sequential decision problem is hard because the state space is a function. We demonstrate that the belief state is well approximated by a Gaussian distribution. We prove a key monotonicity property of the Gaussian state update which makes the problem tractable. The algorithm leads to a surprising insight: an optimal monopolist can provide more liquidity than perfectly competitive market-makers, because a monopolist is willing to absorb initial losses in order to learn a new valuation rapidly so she can extract higher profits later.
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning

Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as -greedy action selection or Gaussian control noise, but there are many tasks where these methods are insufficient to make any learning progress. Here, we consider more complex heuristics: efficient and scalable exploration strategies that maximize a notion of an agent’s surprise about its experiences via intrinsic motivation. We propose to learn a model of the MDP transition probabilities concurrently with the policy, and to form intrinsic rewards that approximate the KL-divergence of the true transition probabilities from the learned model. One of our approximations results in using surprisal as intrinsic motivation, while the other gives the k-step learning progress. We show that our incentives enable agents to succeed in a wide range of environments with high-dimensional state spaces and very sparse rewards, including continuous control tasks and games in the Atari RAM domain, outperforming several other heuristic exploration techniques.
What Do Recurrent Neural Network Grammars Learn About Syntax?

Recurrent neural network grammars (RNNG) are a recently proposed probabilistic generative modeling family for natural language. They show state-ofthe-art language modeling and parsing performance. We investigate what information they learn, from a linguistic perspective, through various ablations to the model and the data, and by augmenting the model with an attention mechanism (GA-RNNG) to enable closer inspection. We find that explicit modeling of composition is crucial for achieving the best performance. Through the attention mechanism, we find that headedness plays a central role in phrasal representation (with the model’s latent attention largely agreeing with predictions made by hand-crafted head rules, albeit with some important differences). By training grammars without nonterminal labels, we find that phrasal representations depend minimally on nonterminals, providing support for the endocentricity hypothesis.
Face Attention Network: An Effective Face Detector for the Occluded Faces

The performance of face detection has been largely improved with the development of convolutional neural network. However, the occlusion issue due to mask and sunglasses, is still a challenging problem. The improvement on the recall of these occluded cases usually brings the risk of high false positives. In this paper, we present a novel face detector called Face Attention Network (FAN), which can significantly improve the recall of the face detection problem in the occluded case without compromising the speed. More specifically, we propose a new anchor-level attention, which will highlight the features from the face region. Integrated with our anchor assign strategy and data augmentation techniques, we obtain state-of-art results on public face ∗Equal contribution. †Work was done during an internship at Megvii Research. detection benchmarks like WiderFace and MAFA. The code will be released for reproduction.
Self-interference cancellation with nonlinear distortion suppression for full-duplex systems

In full-duplex systems, due to the strong self-interference signal, system nonlinearities become a significant limiting factor that bounds the possible cancellable self-interference power. In this paper, a self-interference cancellation scheme for full-duplex orthogonal frequency division multiplexing systems is proposed. The proposed scheme increases the amount of cancellable self-interference power by suppressing the distortion caused by the transmitter and receiver nonlinearities. An iterative technique is used to jointly estimate the self-interference channel and the nonlinearity coefficients required to suppress the distortion signal. The performance is numerically investigated showing that the proposed scheme achieves a performance that is less than 0.5dB off the performance of a linear full-duplex system.
Learning from Incomplete Ratings Using Non-negative Matrix Factorization

We use a low-dimensional linear model to describe the user rating matrix in a recommendation system. A non-negativity constraint is enforced in the linear model to ensure that each user’s rating profile can be represented as an additive linear combination of canonical coordinates. In order to learn such a constrained linear model from an incomplete rating matrix, we introduce two variations on Non-negative Matrix Factorization (NMF): one based on the Expectation-Maximization (EM) procedure and the other a Weighted Nonnegative Matrix Factorization (WNMF). Based on our experiments, the EM procedure converges well empirically and is less susceptible to the initial starting conditions than WNMF, but the latter is much more computationally efficient. Taking into account the advantages of both algorithms, a hybrid approach is presented and shown to be effective in real data sets. Overall, the NMF-based algorithms obtain the best prediction performance compared with other popular collaborative filtering algorithms in our experiments; the resulting linear models also contain useful patterns and features corresponding to user communities.
Cognitive Behavior Therapy for Schizophrenia: Effect Sizes, Clinical Models, and Methodological Rigor

Guidance in the United States and United Kingdom has included cognitive behavior therapy for psychosis (CBTp) as a preferred therapy. But recent advances have widened the CBTp targets to other symptoms and have different methods of provision, eg, in groups.



To explore the effect sizes of current CBTp trials including targeted and nontargeted symptoms, modes of action, and effect of methodological rigor.



Thirty-four CBTp trials with data in the public domain were used as source data for a meta-analysis and investigation of the effects of trial methodology using the Clinical Trial Assessment Measure (CTAM).



There were overall beneficial effects for the target symptom (33 studies; effect size = 0.400 [95% confidence interval [CI] = 0.252, 0.548]) as well as significant effects for positive symptoms (32 studies), negative symptoms (23 studies), functioning (15 studies), mood (13 studies), and social anxiety (2 studies) with effects ranging from 0.35 to 0.44. However, there was no effect on hopelessness. Improvements in one domain were correlated with improvements in others. Trials in which raters were aware of group allocation had an inflated effect size of approximately 50%-100%. But rigorous CBTp studies showed benefit (estimated effect size = 0.223; 95% CI = 0.017, 0.428) although the lower end of the CI should be noted. Secondary outcomes (eg, negative symptoms) were also affected such that in the group of methodologically adequate studies the effect sizes were not significant.



As in other meta-analyses, CBTp had beneficial effect on positive symptoms. However, psychological treatment trials that make no attempt to mask the group allocation are likely to have inflated effect sizes. Evidence considered for psychological treatment guidance should take into account specific methodological detail.
Ordering-sensitive and Semantic-aware Topic Modeling

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the “bag-of-words” assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.
Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach

In this paper, we investigate a challenging task of automatic related work generation. Given multiple reference papers as input, the task aims to generate a related work section for a target paper. The generated related work section can be used as a draft for the author to complete his or her final related work section. We propose our Automatic Related Work Generation system called ARWG to address this task. It first exploits a PLSA model to split the sentence set of the given papers into different topic-biased parts, and then applies regression models to learn the importance of the sentences. At last it employs an optimization framework to generate the related work section. Our evaluation results on a test set of 150 target papers along with their reference papers show that our proposed ARWG system can generate related work sections with better quality. A user study is also performed to show ARWG can achieve an improvement over generic multi-document summarization baselines.
Efficient and extensible security enforcement using dynamic data flow analysis

Current taint tracking systems suffer from high overhead and a lack of generality. In this paper, we solve both of these issues with an extensible system that is an order of magnitude more efficient than previous software taint tracking systems and is fully general to dynamic data flow tracking problems. Our system uses a compiler to transform untrusted programs into policy-enforcing programs, and our system can be easily reconfigured to support new analyses and policies without modifying the compiler or runtime system. Our system uses a sound and sophisticated static analysis that can dramatically reduce the amount of data that must be dynamically tracked. For server programs, our system's average overhead is 0.65% for taint tracking, which is comparable to the best hardware-based solutions. For a set of compute-bound benchmarks, our system produces no runtime overhead because our compiler can prove the absence of vulnerabilities, eliminating the need to dynamically track taint. After modifying these benchmarks to contain format string vulnerabilities, our system's overhead is less than 13%, which is over 6X lower than the previous best solutions. We demonstrate the flexibility and power of our system by applying it to file disclosure vulnerabilities, a problem that taint tracking cannot handle. To prevent such vulnerabilities, our system introduces an average runtime overhead of 0.25% for three open source server programs.
FINDING A MAXIMUM CUT OF A PLANAR GRAPH IN POLYNOMIAL TIME *

The problem of finding a maximum cut of an arbitrary graph is one of a list of 21 com-binatorial problems (Karp-Cook list). It is unknown whether or not there exist algorithms operating in polynomial bounded time for any of these problems. It has been shown that existence for one implies existence for all. In this paper we deal with a special case of the maximum cut problem. By requiring the graph to be planar, it is shown the problem can be translated into a maximum weighted matching problem for which there exists a polynomial bounded algorithm.
Randomized structure from motion based on atomic 3D models from camera triplets

This paper presents a new efficient technique for large-scale structure from motion from unordered data sets. We avoid costly computation of all pairwise matches and geometries by sampling pairs of images using the pairwise similarity scores based on the detected occurrences of visual words leading to a significant speedup. Furthermore, atomic 3D models reconstructed from camera triplets are used as the seeds which form the final large-scale 3D model when merged together. Using three views instead of two allows us to reveal most of the outliers of pairwise geometries at an early stage of the process hindering them from derogating the quality of the resulting 3D structure at later stages. The accuracy of the proposed technique is shown on a set of 64 images where the result of the exhaustive technique is known. Scalability is demonstrated on a landmark reconstruction from hundreds of images.
Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy

Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the identification and removal of premalignant lesions through optical colonoscopy. Unfortunately, conventional colonoscopy misses more than 20% of the polyps that should be removed, due in part to poor contrast of lesion topography. Imaging depth and tissue topography during a colonoscopy is difficult because of the size constraints of the endoscope and the deforming mucosa. Most existing methods make unrealistic assumptions which limits accuracy and sensitivity. In this paper, we present a method that avoids these restrictions, using a joint deep convolutional neural network-conditional random field (CNN-CRF) framework for monocular endoscopy depth estimation. Estimated depth is used to reconstruct the topography of the surface of the colon from a single image. We train the unary and pairwise potential functions of a CRF in a CNN on synthetic data, generated by developing an endoscope camera model and rendering over 200,000 images of an anatomically-realistic colon.We validate our approach with real endoscopy images from a porcine colon, transferred to a synthetic-like domain via adversarial training, with ground truth from registered computed tomography measurements. The CNN-CRF approach estimates depths with a relative error of 0.152 for synthetic endoscopy images and 0.242 for real endoscopy images. We show that the estimated depth maps can be used for reconstructing the topography of the mucosa from conventional colonoscopy images. This approach can easily be integrated into existing endoscopy systems and provides a foundation for improving computer-aided detection algorithms for detection, segmentation and classification of lesions.