nils-herrmann/clean_scirepeval huggingface dataset:
Explanations for the Decline in Child Sexual Abuse Cases

The Office of Juvenile Justice and Delinquency Prevention (OJJDP) is committed to improving the justice system’s response to crimes against children. OJJDP recognizes that children are at increased risk for crime victimization. Not only are children the victims of many of the same crimes that victimize adults, they are subject to other crimes, like child abuse and neglect, that are specific to childhood. The impact of these crimes on young victims can be devastating, and the violent or sexual victimization of children can often lead to an intergenerational cycle of violence and abuse. The purpose of OJJDP’s Crimes Against Children Series is to improve and expand the nation’s efforts to better serve child victims by presenting the latest information about child victimization, including analyses of crime victimization statistics, studies of child victims and their special needs, and descriptions of programs and approaches that address these needs. A Message From OJJDP
Learning to See by Moving

The current dominant paradigm for feature learning in computer vision relies on training neural networks for the task of object recognition using millions of hand labelled images. Is it also possible to learn features for a diverse set of visual tasks using any other form of supervision? In biology, living organisms developed the ability of visual perception for the purpose of moving and acting in the world. Drawing inspiration from this observation, in this work we investigated if the awareness of egomotion(i.e. self motion) can be used as a supervisory signal for feature learning. As opposed to the knowledge of class labels, information about egomotion is freely available to mobile agents. We found that using the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on the tasks of scene recognition, object recognition, visual odometry and keypoint matching.
Emergence of Scaling in Random Networks

www.sciencemag.org (this information is current as of September 11, 2008 ): The following resources related to this article are available online at http://www.sciencemag.org/cgi/content/full/286/5439/509 version of this article at: including high-resolution figures, can be found in the online Updated information and services, found at: can be related to this article A list of selected additional articles on the Science Web sites http://www.sciencemag.org/cgi/content/full/286/5439/509#related-content http://www.sciencemag.org/cgi/content/full/286/5439/509#otherarticles , 3 of which can be accessed for free: cites 11 articles This article 2718 article(s) on the ISI Web of Science. cited by This article has been http://www.sciencemag.org/cgi/content/full/286/5439/509#otherarticles 95 articles hosted by HighWire Press; see: cited by This article has been http://www.sciencemag.org/cgi/collection/physics
A Virtual SDN-Enabled LTE EPC Architecture: A Case Study for S-/P-Gateways Functions

The recent initiative of Network Functions Virtualization (NFV) aims to deliver any data- plane processing or control-plane function in high volume data centers or network elements to decrease operational cost and increase deployment flexibility. In order to dynamically direct traffic flows between respective network elements, Software Defined Networking (SDN) can be seen as one enabler. In this paper, we focus on mobile core network nodes such as the MME, HSS, S- and P- Gateway as standardized for the LTE Evolved Packet Core (EPC). One straightforward solution for a virtualized EPC architecture would be to move all EPC network nodes completely into a data center and handle the data traffic via SDN-enabled switches. However, this solution would keep the conventional monolithic architecture unchanged. A possible split in the EPC functionality between a centralized data center and operator's transport network elements could be needed to provide the desired flexibility, performance and TCO reduction. Therefore, we have analyzed the EPC nodes and classified their functions according to their impact on data-plane and control-plane processing. We propose a mapping for these functions on four alternative deployment frameworks based on SDN and OpenFlow (OF). In addition, we investigate the current OF implementation's capability to realize basic core operations such as QoS, data classification, tunneling and charging. Our analysis shows that functions, which involve high data packet processing such as tunneling, have more potential to be kept on the data-plane network element, i.e. realized by an OpenFlow Switch. We argue for an enhanced OF network element NE+, which contains additional network functions next to the basic OpenFlow protocol.
Input/Output Devices and Interaction Techniques

The computing literature often draws a sharp distinction between input and output; computer scientists are used to regarding a screen as a passive output device and a mouse as a pure input device. However, nearly all examples of human-computer interaction require both input and output to do anything useful. For example, what good would a mouse be without the corresponding feedback embodied by the cursor on the screen, as well as the sound and feel of the buttons when they are clicked? The distinction between output devices and input devices becomes even more blurred in the real world. A sheet of paper can be used to both record ideas (input) and display them (output). Clay reacts to the sculptor’s fingers yet also provides feedback through the curvature and texture of its surface. Indeed, the complete and seamless integration of input and output is becoming a common research theme in advanced computer interfaces such as ubiquitous computing (Weiser, 1991) and tangible interaction (Ishii & Ullmer, 1997).
On mining cross-graph quasi-cliques

Joint mining of multiple data sets can often discover interesting, novel, and reliable patterns which cannot be obtained solely from any single source. For example, in cross-market customer segmentation, a group of customers who behave similarly in multiple markets should be considered as a more coherent and more reliable cluster than clusters found in a single market. As another example, in bioinformatics, by joint mining of gene expression data and protein interaction data, we can find clusters of genes which show coherent expression patterns and also produce interacting proteins. Such clusters may be potential pathways.In this paper, we investigate a novel data mining problem, mining cross-graph quasi-cliques, which is generalized from several interesting applications such as cross-market customer segmentation and joint mining of gene expression data and protein interaction data. We build a general model for mining cross-graph quasi-cliques, show why the complete set of cross-graph quasi-cliques cannot be found by previous data mining methods, and study the complexity of the problem. While the problem is difficult, we develop an efficient algorithm, Crochet, which exploits several interesting and effective techniques and heuristics to efficaciously mine cross-graph quasi-cliques. A systematic performance study is reported on both synthetic and real data sets. We demonstrate some interesting and meaningful cross-graph quasi-cliques in bioinformatics. The experimental results also show that algorithm Crochet is efficient and scalable.
Using Noun Phrase Heads to Extract Document Keyphrases

Automatically extracting keyphrases from documents is a task with many applications in information retrieval and natural language processing. Document retrieval can be biased towards documents containing relevant keyphrases; documents can be classified or categorized based on their keyphrases; automatic text summarization may extract sentences with high keyphrase scores. This paper describes a simple system for choosing noun phrases from a document as keyphrases. A noun phrase is chosen based on its length, its frequency and the frequency of its head noun. Noun phrases are extracted from a text using a base noun phrase skimmer and an off-the-shelf online dictionary. Experiments involving human judges reveal several interesting results: the simple noun phrase-based system performs roughly as well as a state-of-the-art, corpus-trained keyphrase extractor; ratings for individual keyphrases do not necessarily correlate with ratings for sets of keyphrases for a document; agreement among unbiased judges on the keyphrase rating task is poor.
Efficient Selection of Vector Instructions Using Dynamic Programming

Accelerating program performance via SIMD vector units is very common in modern processors, as evidenced by the use of SSE, MMX, VSE, and VSX SIMD instructions in multimedia, scientific, and embedded applications. To take full advantage of the vector capabilities, a compiler needs to generate efficient vector code automatically. However, most commercial and open-source compilers fall short of using the full potential of vector units, and only generate vector code for simple innermost loops. In this paper, we present the design and implementation of anauto-vectorization framework in the back-end of a dynamic compiler that not only generates optimized vector code but is also well integrated with the instruction scheduler and register allocator. The framework includes a novel{\em compile-time efficient dynamic programming-based} vector instruction selection algorithm for straight-line code that expands opportunities for vectorization in the following ways: (1) {\em scalar packing} explores opportunities of packing multiple scalar variables into short vectors, (2)judicious use of {\em shuffle} and {\em horizontal} vector operations, when possible, and (3) {\em algebraic reassociation} expands opportunities for vectorization by algebraic simplification. We report performance results on the impact of auto-vectorization on a set of standard numerical benchmarks using the Jikes RVM dynamic compilation environment. Our results show performance improvement of up to 57.71\% on an Intel Xeon processor, compared tonon-vectorized execution, with a modest increase in compile-time in the range from 0.87\% to 9.992\%. An investigation of the SIMD parallelization performed by v11.1 of the Intel Fortran Compiler (IFC) on three benchmarks shows that our system achieves speedup with vectorization in all three cases and IFC does not. Finally, a comparison of our approach with an implementation of the Super word Level Parallelization (SLP) algorithm from~\cite{larsen00}, shows that our approach yields a performance improvement of up to 13.78\% relative to SLP.
Decoding Customer-Firm Relationships: How Attachment Styles Help Explain Customers' Preferences for Closeness, Repurchase Intentions, and Changes in Relationship Breadth

Many firms strive to create relationships with customers, but not all customers are motivated to build close commercial relationships. This article introduces a theoretical framework that explains how relationshipspecific attachment styles account for customers' distinct preferences for closeness and how both attachment styles and preferences for closeness influence loyalty. The authors test their predictions with survey data from 1199 insurance customers and three years of purchase records for 975 of these customers. They find that attachment styles predict customers' preferences for closeness better than established marketing variables do. Moreover, attachment styles and preferences for closeness influence loyalty intentions and behavior, controlling for established antecedents (e.g., relationship quality). Finally, exploring the underlying process, the authors show that preference for closeness partially mediates the effect of attachment styles on cross-buying behavior. This research provides novel customer segmentation criteria and actionable guidelines that managers can use to improve their ability to tailor relationship marketing activities and more effectively allocate resources to match customer preferences.
Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships

Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas.  Answering Communities offer an opportunity for sharing knowledge at a low cost, where communities users, many of whom are domain experts, can potentially provide high-quality solutions to a given problem. In this paper, we propose a framework for finding experts across multiple collaborative networks. We employ the recent techniques of tree-guided learning (via tensor decomposition), and matrix factorization to explore user expertise from past voted posts. Tensor decomposition enables to leverage the latent expertise of users, and the posts and related tags help identify the related areas. The final result is an expertise score for every user on every knowledge area. We experiment on Stack Exchange Networks, a set of question answering websites on different topics with a huge group of users and posts. Experiments show our proposed approach produces steady and premium outputs.
Hybrid decision tree

In this paper, a hybrid learning approach named HDT is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary hybrid decision tree according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. If unordered attributes cannot further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a specific feedforward neural network named FANNC that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the learning task. Moreover, this paper distinguishes three kinds of incremental learning tasks. Two incremental learning procedures designed for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are frequently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.
Reward Shaping via Meta-Learning

Reward shaping is one of the most effective methods to tackle the crucial yet challenging problem of credit assignment in Reinforcement Learning (RL). However, designing shaping functions usually requires much expert knowledge and handengineering, and the difficulties are further exacerbated given multiple similar tasks to solve. In this paper, we consider reward shaping on a distribution of tasks, and propose a general meta-learning framework to automatically learn the efficient reward shaping on newly sampled tasks, assuming only shared state space but not necessarily action space. We first derive the theoretically optimal reward shaping in terms of credit assignment in model-free RL. We then propose a value-based meta-learning algorithm to extract an effective prior over the optimal reward shaping. The prior can be applied directly to new tasks, or provably adapted to the task-posterior while solving the task within few gradient updates. We demonstrate the effectiveness of our shaping through significantly improved learning efficiency and interpretable visualizations across various settings, including notably a successful transfer from DQN to DDPG.
Adaptive query suggestion for difficult queries

Query suggestion is a useful tool to help users formulate better queries. Although this has been found highly useful globally, its effect on different queries may vary. In this paper, we examine the impact of query suggestion on queries of different degrees of difficulty. It turns out that query suggestion is much more useful for difficult queries than easy queries. In addition, the suggestions for difficult queries should rely less on their similarity to the original query. In this paper, we use a learning-to-rank approach to select query suggestions, based on several types of features including a query performance prediction. As query suggestion has different impacts on different queries, we propose an adaptive suggestion approach that makes suggestions only for difficult queries. We carry out experiments on real data from a search engine. Our results clearly indicate that an approach targeting difficult queries can bring higher gain than a uniform suggestion approach.
Unsupervised Metric Fusion Over Multiview Data by Graph Random Walk-Based Cross-View Diffusion

Learning an ideal metric is crucial to many tasks in computer vision. Diverse feature representations may combat this problem from different aspects; as visual data objects described by multiple features can be decomposed into multiple views, thus often provide complementary information. In this paper, we propose a cross-view fusion algorithm that leads to a similarity metric for multiview data by systematically fusing multiple similarity measures. Unlike existing paradigms, we focus on learning distance measure by exploiting a graph structure of data samples, where an input similarity matrix can be improved through a propagation of graph random walk. In particular, we construct multiple graphs with each one corresponding to an individual view, and a cross-view fusion approach based on graph random walk is presented to derive an optimal distance measure by fusing multiple metrics. Our method is scalable to a large amount of data by enforcing sparsity through an anchor graph representation. To adaptively control the effects of different views, we dynamically learn view-specific coefficients, which are leveraged into graph random walk to balance multiviews. However, such a strategy may lead to an over-smooth similarity metric where affinities between dissimilar samples may be enlarged by excessively conducting cross-view fusion. Thus, we figure out a heuristic approach to controlling the iteration number in the fusion process in order to avoid over smoothness. Extensive experiments conducted on real-world data sets validate the effectiveness and efficiency of our approach.
On pricing discrete barrier options using conditional expectation and importance sampling Monte Carlo

Estimators for the price of a discrete barrier option based on conditional expectation and importance sampling variance reduction techniques are given. There are erroneous formulas for the conditional expectation estimator published in the literature: we derive the correct expression for the estimator. We use a simulated annealing algorithm to estimate the optimal parameters of exponential twisting in importance sampling, and compare it with a heuristic used in the literature. Randomized quasi-Monte Carlo methods are used to further increase the accuracy of the estimators.
Genome‐wide association study discovered genetic variation and candidate genes of fibre quality traits in Gossypium hirsutum L.

Genetic improvement of fibre quality is one of the main breeding goals for the upland cotton, Gossypium hirsutum, but there are difficulties with precise selection of traits. Therefore, it is important to improve the understanding of the genetic basis of phenotypic variation. In this study, we conducted phenotyping and genetic variation analyses of 719 diverse accessions of upland cotton based on multiple environment tests and a recently developed Cotton 63K Illumina Infinium SNP array and performed a genome-wide association study (GWAS) of fibre quality traits. A total of 10 511 polymorphic SNPs distributed in 26 chromosomes were screened across the cotton germplasms, and forty-six significant SNPs associated with five fibre quality traits were detected. These significant SNPs were scattered over 15 chromosomes and were involved in 612 unique candidate genes, many related to polysaccharide biosynthesis, signal transduction and protein translocation. Two major haplotypes for fibre length and strength were identified on chromosomes Dt11 and At07. Furthermore, by combining GWAS and transcriptome analysis, we identified 163 and 120 fibre developmental genes related to length and strength, respectively, of which a number of novel genes and 19 promising genes were screened. These results provide new insight into the genetic basis of fibre quality in G. hirsutum and provide candidate SNPs and genes to accelerate the improvement of upland cotton.
Named Entity Recognition: A Maximum Entropy Approach Using Global Information

This paper presents a maximum entropy-based named entity recognizer (NER). It differs from previous machine learning-based NERs in that it uses information from the whole document to classify each word, with just one classifier. Previous work that involves the gathering of information from the whole document often uses a secondary classifier, which corrects the mistakes of a primary sentencebased classifier. In this paper, we show that the maximum entropy framework is able to make use of global information directly, and achieves performance that is comparable to the best previous machine learning-based NERs on MUC-6 and MUC-7 test data.
Automatic Wood Log Detection Based on Random Decision Forests Learning Algorithm and Histogram of Oriented Gradients

In this article the results of the round wood image recognition problem have been stated. This task is very important in woodworking industry. The paper provides a brief overview of existing solutions of round wood recognition, highs and lows of these approaches are shown. The pattern recognition technique feature for round timber is described. This technique is based on the histogram of oriented gradients (HOG) and random decision forests classifier. Based on the experiments, the optimal number of trees for round timber recognition is found. The analysis of results shows that from 64 trees there is no more appreciable difference between the forests using 128 trees or more. Experimental results have demonstrated a detection rate as high as 95.4% with a false-positive rate 10 -3 .
Multimodal biometric fusion at feature level: Face and palmprint

Multimodal biometrics has recently attracted substantial interest for its high performance in biometric recognition system. In this paper we introduce multimodal biometrics for face and palmprint images using fusion techniques at the feature level. Gabor based image processing is utilized to extract discriminant features, while principal component analysis (PCA) and linear discriminant analysis (LDA) are used to reduce the dimension of each modality. The output features of LDA are serially combined and classified by a Euclidean distance classifier. The experimental results based on ORL face and Poly-U palmprint databases proved that this fusion technique is able to increase biometric recognition rates compared to that produced by single modal biometrics.