nils-herrmann/clean_scirepeval huggingface dataset:
The SpiNNaker Project

The spiking neural network architecture (SpiNNaker) project aims to deliver a massively parallel million-core computer whose interconnect architecture is inspired by the connectivity characteristics of the mammalian brain, and which is suited to the modeling of large-scale spiking neural networks in biological real time. Specifically, the interconnect allows the transmission of a very large number of very small data packets, each conveying explicitly the source, and implicitly the time, of a single neural action potential or “spike.” In this paper, we review the current state of the project, which has already delivered systems with up to 2500 processors, and present the real-time event-driven programming model that supports flexible access to the resources of the machine and has enabled its use by a wide range of collaborators around the world.
Efficient Image Dehazing with Boundary Constraint and Contextual Regularization

Images captured in foggy weather conditions often suffer from bad visibility. In this paper, we propose an efficient regularization method to remove hazes from a single input image. Our method benefits much from an exploration on the inherent boundary constraint on the transmission function. This constraint, combined with a weighted L1-norm based contextual regularization, is modeled into an optimization problem to estimate the unknown scene transmission. A quite efficient algorithm based on variable splitting is also presented to solve the problem. The proposed method requires only a few general assumptions and can restore a high-quality haze-free image with faithful colors and fine image details. Experimental results on a variety of haze images demonstrate the effectiveness and efficiency of the proposed method.
Writing narrative style literature reviews

Reviews provide a synthesis of published literature on a topic and describe its current state-of-art. Reviews in clinical research are thus useful when designing studies or developing practice guidelines. The two standard types of reviews are (a) systematic and (b) non-systematic or narrative review. Unlike systematic reviews that benefit from guidelines such as PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, there are no acknowledged guidelines for narrative reviews. I have attempted to define the best practice recommendations for the preparation of a narrative review in clinical research. The quality of a narrative review may be improved by borrowing from the systematic review methodologies that are aimed at reducing bias in the selection of articles for review and employing an effective bibliographic research strategy. The dynamics of narrative review writing, the organizational pattern of the text, the analysis, and the synthesis processes are also discussed.
A hybrid bipartite graph based recommendation algorithm for mobile games

With the rapid development of the mobile games, mobile game recommendation has become a core technique to mobile game marketplaces. This paper proposes a bipartite graph based recommendation algorithm PKBBR (Prior Knowledge Based for Bipartite Graph Rank). We model the user's interest in mobile game based on bipartite graph structure and use the users' mobile game behavior to describe the edge weights of the graph, then incorporate users' prior knowledge into the projection procedure of the bipartite graph to enrich the information among the nodes. Because the popular games have a great influence on mobile game marketplace, we design a hybrid recommendation algorithm to incorporate popularity recommendation based on users' behaviors. The experiment results show that this hybrid method could achieve a better performance than other approaches.
Robust moving least-squares fitting with sharp features

1. Briefly summarize the paper’s contributions. Does it address a new problem? Does it present a new approach? Does it show new types of results?  [AS] This paper presents a new robust moving least squares (MLS) approach to define a surface from a potentially noisy point set. Using techniques from robust statistics to determine neighborhoods used by MLS, this approach produces a piecewise smooth surface, and is therefore able to preserve sharp features.  [DS] The paper introduces a robust moving least-squares technique for reconstructing a piecewise smooth surface from a noisy point cloud. The method introduces the use of a new robust statistics method for outlier detection: the forward-search paradigm. The algorithm classifies regions of a point-set into outlier-free smooth regions, which allows for the projection of points onto a locally smooth region. This increases the stability of the projection. The algorithm synthesizes new points that reconstruct sharp crease features, which are not part of the input set.  [FP] The authors propose a method to do point projection on a piecewise smooth surface. The authors decompose the set of samples in several regions using robust statistical methods (Least Median Squares and Forward Search) and project the point to the closest valid region. The results presented by the authors show reconstructions with sharper edges than global MLS (e.g., PSS) and also seems to be more robust to noise and outliers.  [JD] The paper presents a method for point set surfaces that detects sharp features. Previous work smoothed sharp features away with noisy samples. This work allows a surface with sharp edges to be reconstructed from a noisy input point set.  [LF] The paper introduces the forward-search paradigm, a robust statistics method for outlier detection, as a technique for dealing with noise and retaining sharp features when computing an MLS-surface.  [MK] The paper proposes two new contributions to the area of surface reconstruction. First, it describes a robust forward-search paradigm to generate reconstructions that are stable in the presence of noise. Second, it extends the MLS framework by supporting the reconstruction of surfaces with sharp features.
The convergence of TD(λ) for general λ

The method of temporal differences (TD) is one way of making consistent predictions about the future. This paper uses some analysis of Watkins (1989) to extend a convergence theorem due to Sutton (1988) from the case which only uses information from adjacent time steps to that involving information from arbitrary ones. It also considers how this version of TD behaves in the face of linearly dependent representations for states—demonstrating that it still converges, but to a different answer from the least mean squares algorithm. Finally it adapts Watkins' theorem that Q-learning, his closely related prediction and action learning method, converges with probability one, to demonstrate this strong form of convergence for a slightly modified version of TD.
Chinese Image Text Recognition on grayscale pixels

This paper presents a novel scheme for Chinese text recognition in images and videos. It's different from traditional paradigms that binarize text images, fed the binarized text to an OCR engine and get the recognized results. The proposed scheme, named grayscale based Chinese Image Text Recognition (gCITR), implements the recognition directly on grayscale pixels via the following steps: image text over-segmentation, building recognition graph, Chinese character recognition and beam search determination. The advantages of gCITR lie in: (1) it does not heavily rely on the performance of binarization, which is not robust in practical and thus severely affects the performance of OCR, (2) grayscale image retains more information of the text thus facilitates the recognition. Experimental results on text from 13 TV news videos demonstrate the effectiveness of the proposed gCITR, from which significant performance gains are observed.
Compiling Knowledge into Decomposable Negation Normal Form

We propose a method for compiling proposit ional theories into a new tractable form that we refer to as decomposable negation normal form (DNNF). We show a number of results about our compilation approach. First, we show that every propositional theory can be compiled into D N N F and present an algorithm to this effect. Second, we show that if a clausal form has a bounded treewidth, then its DNNF compilation has a linear size and can be computed in linear t ime — treewidth is a graphtheoretic parameter which measures the connectivity of the clausal form. Th i rd , we show that once a propositional theory is compiled into DNNF, a number of reasoning tasks, such as satisfiability and forgett ing, can be performed in linear t ime. Finally, we propose two techniques for approximating the DNNF compilat ion of a theory when the size of such compilation is too large to be practical. One of the techniques generates a sound but incomplete compilation, while the other generates a complete but unsound compilation. Together, these approximations bound the exact compilation from below and above in terms for their abil i ty to answer queries.
The child sexual abuse accommodation syndrome.

Child victims of sexual abuse face secondary trauma in the crisis of discovery. Their attempts to reconcile their private experiences with the realities of the outer world are assaulted by the disbelief, blame and rejection they experience from adults. The normal coping behavior of the child contradicts the entrenched beliefs and expectations typically held by adults, stigmatizing the child with charges of lying, manipulating or imagining from parents, courts and clinicians. Such abandonment by the very adults most crucial to the child's protection and recovery drives the child deeper into self-blame, self-hate, alienation and revictimization. In contrast, the advocacy of an empathic clinician within a supportive treatment network can provide vital credibility and endorsement for the child. Evaluation of the responses of normal children to sexual assault provides clear evidence that societal definitions of "normal" victim behavior are inappropriate and procrustean, serving adults as mythic insulators against the child's pain. Within this climate of prejudice, the sequential survival options available to the victim further alienate the child from any hope of outside credibility or acceptance. Ironically, the child's inevitable choice of the "wrong" options reinforces and perpetuates the prejudicial myths. The most typical reactions of children are classified in this paper as the child sexual abuse accommodation syndrome. The syndrome is composed of five categories, of which two define basic childhood vulnerability and three are sequentially contingent on sexual assault: (1) secrecy, (2) helplessness, (3) entrapment and accommodation, (4) delayed, unconvincing disclosure, and (5) retraction. The accommodation syndrome is proposed as a simple and logical model for use by clinicians to improve understanding and acceptance of the child's position in the complex and controversial dynamics of sexual victimization. Application of the syndrome tends to challenge entrenched myths and prejudice, providing credibility and advocacy for the child within the home, the courts, and throughout the treatment process. The paper also provides discussion of the child's coping strategies as analogs for subsequent behavioral and psychological problems, including implications for specific modalities of treatment.
A <formula formulatype="inline"><tex Notation="TeX">$Ka$</tex> </formula>-Band Fully Tunable Cavity Filter

A TE011Ka-band tunable filter with a stable and continuous tuning performance is presented in this paper. Both bandwidth and center-frequency tunability are demonstrated by cascading six-pole pseudo-low-pass and pseudo-high-pass tunable filters. A novel mode-splitter resonator and coupling configuration enabling cross-coupled planar TE011 filter realization is introduced in this paper. The concept can be applied to back-to-back coupled TE011 resonators as well. The filter design is verified through fabrication of multiple tunable filters that demonstrates 500 MHz of tuning range with a stable RF tuning performance.
A Deep Generative Framework for Paraphrase Generation

Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this paper, we address the problem of generating paraphrases automatically. Our proposed method is based on a combination of deep generative models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases, given an input sentence. Traditional VAEs when combined with recurrent neural networks can generate free text but they are not suitable for paraphrase generation for a given sentence. We address this problem by conditioning the both, encoder and decoder sides of VAE, on the original sentence, so that it can generate the given sentence’s paraphrases. Unlike most existing models, our model is simple, modular and can generate multiple paraphrases, for a given sentence. Quantitative evaluation of the proposed method on a benchmark paraphrase dataset demonstrates its efficacy, and its performance improvement over the state-of-the-art methods by a significant margin, whereas qualitative human evaluation indicate that the generated paraphrases are well-formed, grammatically correct, and are relevant to the input sentence. Furthermore, we evaluate our method on a newly released question paraphrase dataset, and establish a new baseline for future research.
A system model for energy efficient green-IoT network

The notion of IoT has ignited umpteen possibilities of different heterogeneous devices to blend within a network. In IoT, especially sensor devices are mostly deployed in an extremely resource constrained environment and thus pose the necessity to extend the capability and life expectancy of these kind of devices in terms of energy consumption. Since, the devices in IoT have limited energy sources they often run on battery with a certain energy capability, the deployment of green communication and system model in IoT has been a core challenging issue. This paper addresses the energy efficiency issues across diverse IoT driven networks by proposing a system model for G-IoT and energy efficient scheme for the IoT devices to extend the life expectancy of the whole IoT network.
Predicting box-office success of motion pictures with neural networks

Predicting box-office receipts of a particular motion picture has intrigued many scholars and industry leaders as a difficult and challenging problem. In this study, the use of neural networks in predicting the financial performance of a movie at the box-office before its theatrical release is explored. In our model, the forecasting problem is converted into a classification problem-rather than forecasting the point estimate of box-office receipts, a movie based on its box-office receipts in one of nine categories is classified, ranging from a ‘flop’ to a ‘blockbuster.’ Because our model is designed to predict the expected revenue range of a movie before its theatrical release, it can be used as a powerful decision aid by studios, distributors, and exhibitors. Our prediction results is presented using two performance measures: average percent success rate of classifying a movie’s success exactly, or within one class of its actual performance. Comparison of our neural network to models proposed in the recent literature as well as other statistical techniques using a 10-fold cross validation methodology shows that the neural networks do a much better job of predicting in this setting.
Free-form anisotropy: A new method for crack detection on pavement surface images

This paper presents a new measure which takes into accounts simultaneously brightness and connectivity, in the segmentation step, for crack detection on road pavement images. Features which are calculated along every free-form paths provide detection of cracks with any form and any orientation. The method proposed does not need learning stage of free defect texture to perform default detection. Experimental results were conducted on some samples of different kinds of pavements. Results of the method are also given on other kinds of images and can provide perspectives on other domains as road extraction on satellite images or segment blood vessels in retinal images.
From presence to consciousness through virtual reality

Immersive virtual environments can break the deep, everyday connection between where our senses tell us we are and where we are actually located and whom we are with. The concept of 'presence' refers to the phenomenon of behaving and feeling as if we are in the virtual world created by computer displays. In this article, we argue that presence is worthy of study by neuroscientists, and that it might aid the study of perception and consciousness.
ISUALIZING A F RAMEWORK FOR T ANGIBILITY IN M ULTIMEDIA L EARNING FOR P RESCHOOLERS

This paper represents an attempt to conceive a tangible multimedia framework in educational settings. For preschoolers, the major drawback in conventional multimedia systems nowadays is the lack of concrete elements that allow them to learn naturally and tangibly. As Piaget stated that children aged below seven can only cognizant concrete objects (preoperational stage), there exists a large gap between preschoolers and digital multimedia. To bridge the gap, multimedia should and need to embrace tangible objects in learning. However, as a newly explored area, there is absence of discussion on how the tangible multimedia should look like. In response to this, a relevant framework that outlines the basic structure of such multimedia is visualized in this paper for reference as well as grounding guidelines. Relevant pilot study conducted revealed that tangible multimedia was efficacious in elevating preschoolers’ learning performance, and motivation.
MilkyWay-2 supercomputer: system and application

On June 17, 2013, MilkyWay-2 (Tianhe-2) supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design of hardware and software systems. The key architecture features of MilkyWay-2 are highlighted, including neo-heterogeneous compute nodes integrating commodity-off-the-shelf processors and accelerators that share similar instruction set architecture, powerful networks that employ proprietary interconnection chips to support the massively parallel message-passing communications, proprietary 16-core processor designed for scientific computing, efficient software stacks that provide high performance file system, emerging programming model for heterogeneous systems, and intelligent system administration. We perform extensive evaluation with wide-ranging applications from LINPACK and Graph500 benchmarks to massively parallel software deployed in the system.
Friends only: examining a privacy-enhancing behavior in facebook

Privacy practices in social network sites often appear paradoxical, as content-sharing behavior stands in conflict with the need to reduce disclosure-related harms. In this study we explore privacy in social network sites as a contextual information practice, managed by a process of boundary regulation. Drawing on a sample survey of undergraduate Facebook users, we examine a particular privacy-enhancing practice: having a friends-only Facebook profile. Particularly, we look at the association between network composition, expectancy violations, interpersonal privacy practices and having a friends-only profile. We find that expectancy violations by weak ties and increased levels of interpersonal privacy management are positively associated with having a friends-only profile. We conclude with a discussion of how these findings may be integrated into the design of systems to facilitate interaction while enhancing individual privacy.
Information-Theoretic Limitations of Formal Systems

An attempt is made to apply information-theoretic computational complexity to meta-mathematics. The paper studies the number of bits of instructions that must be given to a computer for it to perform finite and infinite tasks, and also the time it takes the computer to perform these tasks. This is applied to measuring the difficulty of proving a given set of theorems, in terms of the number of bits of axioms that are assumed, and the size of the proofs needed to deduce the theorems from the axioms.
Characterizing web content, user interests, and search behavior by reading level and topic

A user's expertise or ability to understand a document on a given topic is an important aspect of that document's relevance. However, this aspect has not been well-explored in information retrieval systems, especially those at Web scale where the great diversity of content, users, and tasks presents an especially challenging search problem. To help improve our modeling and understanding of this diversity, we apply automatic text classifiers, based on reading difficulty and topic prediction, to estimate a novel type of profile for important entities in Web search -- users, websites, and queries. These profiles capture topic and reading level distributions, which we then use in conjunction with search log data to characterize and compare different entities.
 We find that reading level and topic distributions provide an important new representation of Web content and user interests, and that using both together is more effective than using either one separately. In particular we find that: 1) the reading level of Web content and the diversity of visitors to a website can vary greatly by topic; 2) the degree to which a user's profile matches with a site's profile is closely correlated with the user's preference of the website in search results, and 3) site or URL profiles can be used to predict 'expertness' whether a given site or URL is oriented toward expert vs. non-expert users. Our findings provide strong evidence in favor of jointly incorporating reading level and topic distribution metadata into a variety of critical tasks in Web information systems.
Hipikat: a project memory for software development

Sociological and technical difficulties, such as a lack of informal encounters, can make it difficult for new members of noncollocated software development teams to learn from their more experienced colleagues. To address this situation, we have developed a tool, named Hipikat that provides developers with efficient and effective access to the group memory for a software development project that is implicitly formed by all of the artifacts produced during the development. This project memory is built automatically with little or no change to existing work practices. After describing the Hipikat tool, we present two studies investigating Hipikat's usefulness in software modification tasks. One study evaluated the usefulness of Hipikat's recommendations on a sample of 20 modification tasks performed on the Eclipse Java IDE during the development of release 2.1 of the Eclipse software. We describe the study, present quantitative measures of Hipikat's performance, and describe in detail three cases that illustrate a range of issues that we have identified in the results. In the other study, we evaluated whether software developers who are new to a project can benefit from the artifacts that Hipikat recommends from the project memory. We describe the study, present qualitative observations, and suggest implications of using project memory as a learning aid for project newcomers.