nils-herrmann/clean_scirepeval huggingface dataset:
Nonconvex Weighted $\ell _p$ Minimization Based Group Sparse Representation Framework for Image Denoising

Nonlocal image representation or group sparsity has attracted considerable interest in various low-level vision tasks and has led to several state-of-the-art image denoising techniques, such as BM3D, learned simultaneous sparse coding. In the past, convex optimization with sparsity-promoting convex regularization was usually regarded as a standard scheme for estimating sparse signals in noise. However, using convex regularization cannot still obtain the correct sparsity solution under some practical problems including image inverse problems. In this letter, we propose a nonconvex weighted <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math></inline-formula> minimization based group sparse representation framework for image denoising. To make the proposed scheme tractable and robust, the generalized soft-thresholding algorithm is adopted to solve the nonconvex <inline-formula><tex-math notation="LaTeX"> $\ell _p$</tex-math></inline-formula> minimization problem. In addition, to improve the accuracy of the nonlocal similar patch selection, an adaptive patch search scheme is proposed. Experimental results demonstrate that the proposed approach not only outperforms many state-of-the-art denoising methods such as BM3D and weighted nuclear norm minimization, but also results in a competitive speed.
A Study on the Negative Effects of Social Networking Sites Such as Facebook among Asia Pacific University Scholars in Malaysia

The purpose of this study is to identify the negative effects of social network sites such as Facebook among Asia Pacific University scholars. The researcher, distributed 152 surveys to students of the chosen university to examine and study the negative effects. Electronic communication is emotionally gratifying but how do such technological distraction impact on academic performance? Because of social media platform’s widespread adoption by university students, there is an interest in how Facebook is related to academic performance. This paper measure frequency of use, participation in activities and time spent preparing for class, in order to know if Facebook affects the performance of students. Moreover, the impact of social network site on academic performance also raised another major concern which is health. Today social network sites are running the future and carrier of students. Social network sites were only an electronic connection between users, but unfortunately it has become an addiction for students. This paper examines the relationship between social network sites and health threat. Lastly, the paper provides a comprehensive analysis of the law and privacy of Facebook. It shows how Facebook users socialize on the site, while they are not aware or misunderstand the risk involved and how their privacy suffers as a result.
Drug targeting to myofibroblasts: Implications for fibrosis and cancer.

Myofibroblasts are the key players in extracellular matrix remodeling, a core phenomenon in numerous devastating fibrotic diseases. Not only in organ fibrosis, but also the pivotal role of myofibroblasts in tumor progression, invasion and metastasis has recently been highlighted. Myofibroblast targeting has gained tremendous attention in order to inhibit the progression of incurable fibrotic diseases, or to limit the myofibroblast-induced tumor progression and metastasis. In this review, we outline the origin of myofibroblasts, their general characteristics and functions during fibrosis progression in three major organs: liver, kidneys and lungs as well as in cancer. We will then discuss the state-of-the art drug targeting technologies to myofibroblasts in context of the above-mentioned organs and tumor microenvironment. The overall objective of this review is therefore to advance our understanding in drug targeting to myofibroblasts, and concurrently identify opportunities and challenges for designing new strategies to develop novel diagnostics and therapeutics against fibrosis and cancer.
Query-Adaptive Image Retrieval by Deep-Weighted Hashing

Hashing methods have attracted much attention for large-scale image retrieval. Some deep hashing methods have achieved promising results by taking advantage of the strong representation power of deep networks recently. However, existing deep hashing methods treat all hash bits equally. On one hand, a large number of images share the same distance to a query image due to the discrete Hamming distance, which raises a critical issue of image retrieval where fine-grained rankings are very important. On the other hand, different hash bits actually contribute to the image retrieval differently, and treating them equally greatly affects the retrieval accuracy. To address the above two problems, we propose the query-adaptive deep weighted hashing approach, which can perform fine-grained ranking for different queries by weighted Hamming distance. First, a novel deep hashing network is proposed to learn the hash codes and corresponding classwise weights jointly, so that the learned weights can reflect the importance of different hash bits for different image classes. Second, a query-adaptive image retrieval method is proposed, which rapidly generates hash bit weights for different query images by fusing its semantic probability and the learned classwise weights. Fine-grained image ranking is then performed by the weighted Hamming distance, which can provide more accurate ranking than the traditional Hamming distance. Experiments on four widely used datasets show that the proposed approach outperforms eight state-of-the-art hashing methods.
Personalization of gamification-elements in an e-learning environment based on learners' motivation

Gamified systems benefit from various gamification-elements to motivate users and encourage them to persist in their quests towards a goal. This paper proposes a categorization of gamification-elements and learners' motivation type to enrich a learning management system with the advantages of personalization and gamification. This categorization uses the learners' motivation type to assign gamification-elements in learning environments. To find out the probable relations between gamification-elements and learners' motivation type, a field-research is done to measure learners' motivation along with their interests in gamification-elements. Based on the results of this survey, all the gamification-elements are categorized according to related motivation types, which form our proposed categorization. To investigate the effects of this personalization approach, a gamified learning management system is prepared. Our implemented system is evaluated in Technical English course at University of Tehran. Our experimental results on the average participation rate show the effectiveness of the personalization approach on the learners' motivation. Based on the paper findings, we suggest an integrated categorization of gamification-elements and learners' motivation type, which can further enhance the learners' motivation through personalization.
Abstract semantic differencing via speculative correlation

We address the problem of computing semantic differences between a program and a patched version of the program. Our goal is to obtain a precise characterization of the difference between program versions, or establish their equivalence. We focus on infinite-state numerical programs, and use abstract interpretation to compute an over-approximation of program differences.
 Computing differences and establishing equivalence under abstraction requires abstracting relationships between variables in the two programs. Towards that end, we use a correlating abstract domain to compute a sound approximation of these relationships which captures semantic difference. This approximation can be computed over any interleaving of the two programs. However, the choice of interleaving can significantly affect precision. We present a speculative search algorithm that aims to find an interleaving of the two programs with minimal abstract semantic difference. This method is unique as it allows the analysis to dynamically alternate between several interleavings.
 We have implemented our approach and applied it to real-world examples including patches from Git, GNU Coreutils, as well as a few handpicked patches from the Linux kernel and the Mozilla Firefox web browser. Our evaluation shows that we compute precise approximations of semantic differences, and report few false differences.
2 . Dilated Residual Networks

Convolutional networks for image classification progressively reduce resolution until the image is represented by tiny feature maps in which the spatial structure of the scene is no longer discernible. Such loss of spatial acuity can limit image classification accuracy and complicate the transfer of the model to downstream applications that require detailed scene understanding. These problems can be alleviated by dilation, which increases the resolution of output feature maps without reducing the receptive field of individual neurons. We show that dilated residual networks (DRNs) outperform their non-dilated counterparts in image classification without increasing the model’s depth or complexity. We then study gridding artifacts introduced by dilation, develop an approach to removing these artifacts (‘degridding’), and show that this further increases the performance of DRNs. In addition, we show that the accuracy advantage of DRNs is further magnified in downstream applications such as object localization and semantic segmentation.
Decorrelated Batch Normalization

Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find that PCA whitening causes a problem we call stochastic axis swapping, which is detrimental to learning. We show that ZCA whitening does not suffer from this problem, permitting successful learning. DBN retains the desirable qualities of BN and further improves BN's optimization efficiency and generalization ability. We design comprehensive experiments to show that DBN can improve the performance of BN on multilayer perceptrons and convolutional neural networks. Furthermore, we consistently improve the accuracy of residual networks on CIFAR-10, CIFAR-100, and ImageNet.
Bidirectional LSTM-RNN with Bi-attention for reading comprehension (

12 In this work, we implemented bi-directional LSTM-RNN network to solve 13 the reading comprehension problem. The problem is, given a question and a 14 context (contains the answer to the question), find the answer in the context. 15 Following the method in paper [11], we use bi-attention to make the link 16 from question to context and from context to question, to make good use of 17 the information of relationship between the two parts. By using inner 18 product, we find the probabilities of the context word to be the first or last 19 word of answer. Also, we used some improvement to the paper reducing the 20 training time and improving the accuracy. After adjusting parameters, the 21 best model has performance of F1=48% and EM=33% leaderboard. 22
Pay-per-Question: Towards Targeted Q&A with Payments

Online question and answer (Q&A) services are facing key challenges to motivate domain experts to provide quick and high-quality answers. Recent systems seek to engage real-world experts by allowing them to set a price on their answers. This leads to a "targeted" Q&A model where users to ask questions to a target expert by paying the price. In this paper, we perform a case study on two emerging targeted Q&A systems Fenda (China) and Whale (US) to understand how monetary incentives affect user behavior. By analyzing a large dataset of 220K questions (worth 1 million USD), we find that payments indeed enable quick answers from experts, but also drive certain users to game the system for profits. In addition, this model requires users (experts) to proactively adjust their price to make profits. People who are unwilling to lower their prices are likely to hurt their income and engagement over time.
Continuous Control Monte Carlo Tree Search Informed by Multiple Experts.

Efficient algorithms for 3D character control in continuous control setting remain an open problem in spite of the remarkable recent advances in the field. We present a sampling-based model-predictive controller that comes in the form of a Monte Carlo tree search (MCTS). The tree search utilizes information from multiple sources including two machine learning models. This allows rapid development of complex skills such as 3D humanoid locomotion with less than a million simulation steps, in less than a minute of computing on a modest personal computer. We demonstrate locomotion of 3D characters with varying topologies under disturbances such as heavy projectile hits and abruptly changing target direction. In this paper we also present a new way to combine information from the various sources such that minimal amount of information is lost. We furthermore extend the neural network, involved in the algorithm, to represent stochastic policies. Our approach yields a robust control algorithm that is easy to use. While learning, the algorithm runs in near real-time, and after learning the sampling budget can be reduced for real-time operation.
Multi-scale Fully Convolutional Network for Face Detection in the Wild

Face detection is a classical problem in computer vision. It is still a difficult task due to many nuisances that naturally occur in the wild. In this paper, we propose a multi-scale fully convolutional network for face detection. To reduce computation, the intermediate convolutional feature maps (conv) are shared by every scale model. We up-sample and down-sample the final conv map to approximate K levels of a feature pyramid, leading to a wide range of face scales that can be detected. At each feature pyramid level, a FCN is trained end-to-end to deal with faces in a small range of scale change. Because of the up-sampling, our method can detect very small faces (10×10 pixels). We test our MS-FCN detector on four public face detection datasets, including FDDB, WIDER FACE, AFW and PASCAL FACE. Extensive experiments show that it outperforms state-of-the-art methods. Also, MS-FCN runs at 23 FPS on a GPU for images of size 640×480 with no assumption on the minimum detectable face size.
Storyworld Creation: Authoring For Interactive Storytelling

Storytelling humankind’s universal choice for content transmission is becoming of great importance in the field of computer graphics, as the human ability to keep track of information in the information society of the 21 century is dependent on the quality of the information providing systems. Basically, the first steps towards storytelling systems have been taken; everyone today has the possibility to step into enfolding 3D worlds and become immersed in extensive loads of data. However, there is still a great backlog on the human-like organization of the associated data. The reason for this is the absence of the basic authoring systems for interactive storytelling. This position paper presents an approach to new authoring methods for interactive storytelling. It considers the author’s view of the tools to be used and introduces a coherent environment that does not restrict the creative process and lets the author feel comfortable, leading him to create well-narrated, interactive non-linear stories.
Satisfaction with male-to-female gender reassignment surgery.

The frequency of gender identity disorder is hard to determine; the number of gender reassignment operations and of court proceedings in accordance with the German Law on Transsexuality almost certainly do not fully reflect the underlying reality. There have been only a few studies on patient satisfaction with male-to-female gender reassignment surgery.



254 consecutive patients who had undergone male-to-female gender reassignment surgery at Essen University Hospital's Department of Urology retrospectively filled out a questionnaire about their subjective postoperative satisfaction.



119 (46.9% ) of the patients filled out and returned the questionnaires, at a mean of 5.05 years after surgery (standard deviation 1.61 years, range 1-7 years). 90.2% said their expectations for life as a woman were fulfilled postoperatively. 85.4% saw themselves as women. 61.2% were satisfied, and 26.2% very satisfied, with their outward appearance as a woman; 37.6% were satisfied, and 34.4% very satisfied, with the functional outcome. 65.7% said they were satisfied with their life as it is now.



The very high rates of subjective satisfaction and the surgical outcomes indicate that gender reassignment surgery is beneficial. These findings must be interpreted with caution, however, because fewer than half of the questionnaires were returned.
A REVIEW OF OUT-OF-AUTOCLAVE PREPREGS-MATERIAL PROPERTIES , PROCESS PHENOMENA AND MANUFACTURING CONSIDERATIONS

Out-of-autoclave (OoA) prepreg materials and methods have gained acceptance over the past decade because of the ability to produce autoclave-quality components under vacuum-bag-only (VBO) cure. To achieve low porosity and tight dimensional tolerances, VBO prepregs rely on specific microstructural features and processing techniques. Furthermore, successful cure is contingent upon appropriate material property and process parameter selection. In this article, we review the existing literature on VBO prepreg processing to summarize and synthesize knowledge on these issues. First, the context, development, and defining properties of VBO prepregs are presented. The key processing phenomena and the influence on quality are subsequently described. Finally, cost and environmental performance are considered. Throughout, we highlight key considerations for VBO prepreg processing and identify areas where further study is required.
Presenting Historical Photos using Augmented Reality

Presenting historical content and information illustratively and interestingly for the audience is an interesting challenge. Now that mobile devices with good computational and graphical capabilities have become wide-spread, Augmented Reality (AR) has become an attractive solution. Historical events can be presented for the tourist in the very locations where they occurred. One of the easiest types of historical content to present in AR is historical photographs. This paper presents mobile applications to show historical photos for tourists in Augmented Reality. We present several on-site pilot cases and give a summary of technical findings and feedback from test users.
Game Theory for Network Security

As networks become ubiquitous in people's lives, users depend on networks a lot for sufficient communication and convenient information access. However, networks suffer from security issues. Network security becomes a challenging topic since numerous new network attacks have appeared increasingly sophisticated and caused vast loss to network resources. Game theoretic approaches have been introduced as a useful tool to handle those tricky network attacks. In this paper, we review the existing game-theory based solutions for network security problems, classifying their application scenarios under two categories, attack-defense analysis and security measurement. Moreover, we present a brief view of the game models in those solutions and summarize them into two categories, cooperative game models and non-cooperative game models with the latter category consisting of subcategories. In addition to the introduction to the state of the art, we discuss the limitations of those game theoretic approaches and propose future research directions.
A Universal Measure of Intelligence for Artificial Agents

where ri is the reward in cycle i of a given history, and the expected value is taken over all possible interaction histories of π and μ. The choice of γi is a subtle issue that controls how greedy or far sighted the agent should be. Here we use the near-harmonic γi := 1/i2 as this produces an agent with increasing farsightedness of the order of its current age [Hutter2004]. As we desire an extremely general definition of intelligence for arbitrary systems, our space of environments should be as large as possible. An obvious choice is the space of all probability measures, however this causes serious problems as we cannot even describe some of these measures in a finite way.
Novel Leakage Detection by Ensemble CNN-SVM and Graph-Based Localization in Water Distribution Systems

In many water distribution systems, a significant amount of water is lost because of leakage during transit from the water treatment plant to consumers. As a result, water leakage detection and localization have been a consistent focus of research. Typically, diagnosis or detection systems based on sensor signals incur significant computational and time costs, whereas the system performance depends on the features selected as input to the classifier. In this paper, to solve this problem, we propose a novel, fast, and accurate water leakage detection system with an adaptive design that fuses a one-dimensional convolutional neural network and a support vector machine. We also propose a graph-based localization algorithm to determine the leakage location. An actual water pipeline network is represented by a graph network and it is assumed that leakage events occur at virtual points on the graph. The leakage location at which costs are minimized is estimated by comparing the actual measured signals with the virtually generated signals. The performance was validated on a wireless sensor network based test bed, deployed on an actual WDS. Our proposed methods achieved 99.3% leakage detection accuracy and a localization error of less than 3 m.
Introduction to the Langlands Program

This article is an introduction to automorphic forms on the adeles of a linear reductive group over a number field. The first half is a summary of aspects of local and global class field theory, with emphasis on the local Weil group, the L functions of Artin and Hecke, and the role of Artin reciprocity in relating the two kinds of L functions. The first half serves as background for the second half, which discusses some structure theory for reductive groups, the definitions of automorphic and cusp forms, the Langlands L group, L functions, functoriality, and some conjectures. Much of the material in the second half may be regarded as a brief introduction to the Langlands program. There are ten sections:
Full Attitude Control of a VTOL tailsitter UAV

This paper addresses the challenges of the design, development and control of a new convertible VTOL tailsitter unmanned aerial vehicle that combines the advantages of both fixed wing and rotary wing systems. Wind tunnel measurements are used to get an understanding of the control allocation and to model the static forces and moments acting on the system. Based on the derived model, a novel controller that operates in SO(3) and handles the dynamics of the vehicle at any attitude configuration, including the rotorcraft and fixed-wing regimes as well as their transitions, is presented. This unified controller allows the autonomous transition of the system without discontinuities of switching, as well as its overall high performance flight control. The capabilities and flying qualities of the platform and the controller are demonstrated and evaluated by means of extensive experimental studies.