nils-herrmann/clean_scirepeval huggingface dataset:
Ffsck: The Fast File-System Checker

Failures, errors, and bugs can corrupt file systems and cause data loss, despite the presence of journals and similar preventive techniques. While consistency checkers such as fsck can detect corruption and repair a damaged image, they are generally created as an afterthought, to be run only at rare intervals. Thus, checkers operate slowly, causing significant downtime for large scale storage systems.
 We address this dilemma by treating the checker as a key component of the overall file system, rather than a peripheral add-on. To this end, we present a modified ext3 file system, rext3, to directly support the fast file-system checker, ffsck. Rext3 colocates and self-identifies its metadata blocks, removing the need for costly seeks and tree traversals during checking. These modifications allow ffsck to scan and repair the file system at rates approaching the full sequential bandwidth of the underlying device. In addition, we demonstrate that rext3 generally performs competitively with ext3 and exceeds it in handling random reads and large writes. Finally, we apply our principles to FreeBSD’s FFS file system and its checker, doing so in a lightweight fashion that preserves the file-system layout while still providing some of the performance gains from ffsck.
Using Ontologies to Quantify Attack Surfaces

Cyber security remains one of the most serious challenges to national security and the economy that we face today. Systems employing well known but static defenses are increasingly vulnerable to penetration from determined, diverse, and well resourced adversaries launching targeted attacks such as Advanced Persistent Threats (APTs). Due to the heavy focus on cyber security technologies in both commercial and government environments over the last decade, an overwhelming array of cyber defense technologies have become available for cyber defenders to use. As the number and complexity of these defenses increase, cyber defenders face the problem of selecting, composing, and configuring them, a process which to date is performed manually and without a clear understanding of integration points and risks associated with each defense or combination of defenses. As shown in Figure 1, the current state-of-the-art approach for selecting and configuring cyber defenses is manual in nature and is often done without a clear understanding of security metrics associated with attack surfaces. Due to the talent Unknown& Security& Metrics Manual&Selection&and& Configuration&of&Cyber&Defenses
Anomaly-Based Intrusion Detection for SCADA Systems

Most critical infrastructure such as chemical processing plants, electrical generation and distribution networks, and gas distribution is monitored and controlled by Supervisory Control and Data Acquisition Systems (SCADA). These systems have been the focus of increased security and there are concerns that they could be the target of international terrorists. With the constantly growing number of internet related computer attacks, there is evidence that our critical infrastructure may also be vulnerable. Researchers estimate that malicious online actions may cause $75 billion at 2007. One of the interesting countermeasures for enhancing information system security is called intrusion detection. This paper will briefly discuss the history of research in intrusion detection techniques and introduce the two basic detection approaches: signature detection and anomaly detection. Finally, it presents the application of techniques developed for monitoring critical process systems, such as nuclear power plants, to anomaly intrusion detection. The method uses an autoassociative kernel regression (AAKR) model coupled with the statistical probability ratio test (SPRT) and applied to a simulated SCADA system. The results show that these methods can be generally used to detect a variety of common attacks.
Geocoding for texts with fine-grain toponyms: an experiment on a geoparsed hiking descriptions corpus

Geoparsing and geocoding are two essential middleware services to facilitate final user applications such as location-aware searching or different types of location-based services. The objective of this work is to propose a method for establishing a processing chain to support the geoparsing and geocoding of text documents describing events strongly linked with space and with a frequent use of fine-grain toponyms. The geoparsing part is a Natural Language Processing approach which combines the use of part of speech and syntactico-semantic combined patterns (cascade of transducers). However, the real novelty of this work lies in the geocoding method. The geocoding algorithm is unsupervised and takes profit of clustering techniques to provide a solution for disambiguating the toponyms found in gazetteers, and at the same time estimating the spatial footprint of those other fine-grain toponyms not found in gazetteers. The feasibility of the proposal has been tested with a corpus of hiking descriptions in French, Spanish and Italian.
Enhancing metacognitive reinforcement learning using reward structures and feedback

How do we learn to think better, and what can we do to promote such metacognitive learning? Here, we propose that cognitive growth proceeds through metacognitive reinforcement learning. We apply this theory to model how people learn how far to plan ahead and test its predictions about the speed of metacognitive learning in two experiments. In the first experiment, we find that our model can discern a reward structure that promotes metacognitive reinforcement learning from one that hinders it. In the second experiment, we show that our model can be used to design a feedback mechanism that enhances metacognitive reinforcement learning in an environment that hinders learning. Our results suggest that modeling metacognitive learning is a promising step towards promoting cognitive growth.
Interactive Tensor Field Design and Visualization on Surfaces

Designing tensor fields in the plane and on surfaces is a necessary task in many graphics applications, such as painterly rendering, pen-and-ink sketching of smooth surfaces, and anisotropic remeshing. In this article, we present an interactive design system that allows a user to create a wide variety of symmetric tensor fields over 3D surfaces either from scratch or by modifying a meaningful input tensor field such as the curvature tensor. Our system converts each user specification into a basis tensor field and combines them with the input field to make an initial tensor field. However, such a field often contains unwanted degenerate points which cannot always be eliminated due to topological constraints of the underlying surface. To reduce the artifacts caused by these degenerate points, our system allows the user to move a degenerate point or to cancel a pair of degenerate points that have opposite tensor indices. These operations provide control over the number and location of the degenerate points in the field. We observe that a tensor field can be locally converted into a vector field so that there is a one-to-one correspondence between the set of degenerate points in the tensor field and the set of singularities in the vector field. This conversion allows us to effectively perform degenerate point pair cancellation and movement by using similar operations for vector fields. In addition, we adapt the image-based flow visualization technique to tensor fields, therefore allowing interactive display of tensor fields on surfaces. We demonstrate the capabilities of our tensor field design system with painterly rendering, pen-and-ink sketching of surfaces, and anisotropic remeshing
Examining the effectiveness of real-time query expansion

Interactive query expansion (IQE) (c.f. Efthimiadis, 1996) is a potentially useful technique to help searchers formulate improved query statements, and ultimately retrieve better search results. However, IQE is seldom used in operational settings. Two possible explanations for this are that IQE is generally not integrated into searchers’ established information-seeking behaviors (e.g., examining lists of documents), and it may not be offered at a time in the search when it is needed most (i.e., during the initial query formulation). These challenges can be addressed by coupling IQE more closely with familiar search activities, rather than as a separate functionality that searchers must learn. In this article we introduce and evaluate a variant of IQE known as RealTime Query Expansion (RTQE). As a searcher enters their query in a text box at the interface RTQE provides a list of suggested additional query terms, in effect offering query expansion options while the query is formulated. To investigate how the technique is used – and when it may be useful – we conducted a user study comparing three search interfaces: a baseline interface with no query expansion support; an interface that provides expansion options during query entry, and a third interface that provides options after queries have been submitted to a search system. The results show that offering RTQE leads to better quality initial queries, more engagement in the search, and an increase in the uptake of query expansion. However, the results also imply that care must be taken when implementing RTQE at the interface. Our findings have broad implications for how IQE should be offered, and form part of our research on the development of techniques to support the increased use of query expansion.
Toward proccedural decorative ornamentation in games

Castles, palaces, temples and cathedrals in the real world are densely decorated with ornamentation. Computer games, in contrast, usually have much less and appear Spartan in comparison. Game worlds, whether hand-made or procedural, require greater decorative ornamentation to increase their realism and beauty. Currently artists create this ornament by hand; this doesn't scale. In order to have more decorative artwork in games, procedural algorithms must be developed to generate it, for only this approach will create sufficient quantities, quickly, and at low cost. This paper justifies the importance of decorative ornamentation in computer games and provides an overview of existing research on algorithmic generation of patterns and ornamentation. A series of open research issues demonstrates the breadth of potential research that can be performed in this area. Together, these make the case that procedural decorative ornamentation is a new and interesting research subdomain within the area of procedural content generation for computer games.
Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image Semantic Segmentaion

One of the time-consuming routine work for a radiologist is to discern anatomical structures from tomographic images. For assisting radiologists, this paper develops an automatic segmentation method for pelvic magnetic resonance (MR) images. The task has three major challenges 1) A pelvic organ can have various sizes and shapes depending on the axial image, which requires local contexts to segment correctly. 2) Different organs often have quite similar appearance in MR images, which requires global context to segment. 3) The number of available annotated images are very small to use the latest segmentation algorithms. To address the challenges, we propose a novel convolutional neural network called Attention-Pyramid network (APNet) that effectively exploits both local and global contexts, in addition to a data-augmentation technique that is particularly effective for MR images. In order to evaluate our method, we construct fine-grained (50 pelvic organs) MR image segmentation dataset, and experimentally confirm the superior performance of our techniques over the state-of-the-art image segmentation methods.
A signal processing approach to fair surface design

In this paper we describe a new tool for interactive free-form fair surface design. By generalizing classical discrete Fourier analysis to two-dimensional discrete surface signals – functions defined on polyhedral surfaces of arbitrary topology –, we reduce the problem of surface smoothing, or fairing, to low-pass filtering. We describe a very simple surface signal low-pass filter algorithm that applies to surfaces of arbitrary topology. As opposed to other existing optimization-based fairing methods, which are computationally more expensive, this is a linear time and space complexity algorithm. With this algorithm, fairing very large surfaces, such as those obtained from volumetric medical data, becomes affordable. By combining this algorithm with surface subdivision methods we obtain a very effective fair surface design technique. We then extend the analysis, and modify the algorithm accordingly, to accommodate different types of constraints. Some constraints can be imposed without any modification of the algorithm, while others require the solution of a small associated linear system of equations. In particular, vertex location constraints, vertex normal constraints, and surface normal discontinuities across curves embedded in the surface, can be imposed with this technique.
A Practical Approach to Differential Private Learning

Applying differential private learning to real-world data is currently unpractical. Differential privacy (DP) introduces extra hyper-parameters for which no thorough good practices exist, while manually tuning these hyper-parameters on private data results in low privacy guarantees. Furthermore, the exact guarantees provided by differential privacy for machine learning models are not well understood. Current approaches use undesirable post-hoc privacy attacks on models to assess privacy guarantees. To improve this situation, we introduce three tools to make DP machine learning more practical. First, two sanity checks for differential private learning are proposed. These sanity checks can be carried out in a centralized manner before training, do not involve training on the actual data and are easy to implement. Additionally, methods are proposed to reduce the effective number of tuneable privacy parameters by making use of an adaptive clipping bound. Lastly, existing methods regarding large batch training and differential private learning are combined. It is demonstrated that this combination improves model performance within a constant privacy budget.
Question Answering with Knowledge Base, Web and Beyond

In this tutorial, we give the audience a coherent overview of the research of question answering (QA). We first introduce a variety of QA problems proposed by pioneer researchers and briefly describe the early efforts. By contrasting with the current research trend in this domain, the audience can easily comprehend what technical problems remain challenging and what the main breakthroughs and opportunities are during the past half century. For the rest of the tutorial, we select three categories of the QA problems that have recently attracted a great deal of attention in the research community, and present the tasks with the latest technical survey. We conclude the tutorial by discussing the new opportunities and future directions of QA research.
Fine-to-Coarse Global Registration of RGB-D Scans

RGB-D scanning of indoor environments is important for many applications, including real estate, interior design, and virtual reality. However, it is still challenging to register RGB-D images from a hand-held camera over a long video sequence into a globally consistent 3D model. Current methods often can lose tracking or drift and thus fail to reconstruct salient structures in large environments (e.g., parallel walls in different rooms). To address this problem, we propose a fine-to-coarse global registration algorithm that leverages robust registrations at finer scales to seed detection and enforcement of new correspondence and structural constraints at coarser scales. To test global registration algorithms, we provide a benchmark with 10,401 manually-clicked point correspondences in 25 scenes from the SUN3D dataset. During experiments with this benchmark, we find that our fine-to-coarse algorithm registers long RGB-D sequences better than previous methods.
Dynamic Programming with Convexity, Concavity, and Sparsity

A fast algorithm for computing longest common subse-quences.  Johnson, D. B. A priority queue in which initialization and queue operations take O(log log D) Pattern recognition in nucleic acid sequences II: an ecient method for nding locally stable secondary structures. A simple linear time algorithm for concave one-dimensional dynamic programming.  1990. almost linear time algorithm for generalized matrix searching. On-line dynamic programming with applications to the prediction of RNA secondary structure. A general method applicable to the search for similarities in the amino acid sequence of two proteins. convex and concave cost functions. A linear-time algorithm for concave one-dimensional dynamic programming. On-line context-free language recognition in less than cubic time.
Using Topic Models in Content-Based News Recommender Systems

We study content-based recommendation of Finnish news in a system with a very small group of users. We compare three standard methods, Naïve Bayes (NB), K-Nearest Neighbor (kNN) Regression and Regulairized Linear Regression in a novel online simulation setting and in a coldstart simulation. We also apply Latent Dirichlet Allocation (LDA) on the large corpus of news and compare the learned features to those found by Singular Value Decomposition (SVD). Our results indicate that Naïve Bayes is the worst of the three models. K-Nearest Neighbor performs consistently well across input features. Regularized Linear Regression performs generally worse than kNN, but reaches similar performance as kNN with some features. Regularized Linear Regression gains statistically significant improvements over the word-features with LDA both on the full data set and in the cold-start simulation. In the cold-start simulation we find that LDA gives statistically significant improvements for all the methods.
Embedded Interaction: Interacting with the Internet of Things

The Internet of Things assumes that objects have digital functionality and can be identified and tracked automatically. The main goal of embedded interaction is to look at new opportunities that arise for interactive systems and the immediate value users gain. The authors developed various prototypes to explore novel ways for human-computer interaction (HCI), enabled by the Internet of Things and related technologies. Based on these experiences, they derive a set of guidelines for embedding interfaces into people's daily lives.
Coding Dialogs with the DAMSL Annotation Scheme

This paper describes the DAMSL annotation scheme for communicative acts in dialog. The scheme has three layers: Forward Communicative Functions, Backward Communicative Functions, and Utterance Features. Each layer allows multiple communicative functions of an utterance to be labeled. The Forward Communicative Functions consist of a taxonomy in a similar style as the actions of traditional speech act theory. The Backward Communicative Functions indicate how the current utterance relates to the previous dialog, such as accepting a proposal, con rming understanding, or answering a question. The Utterance Features include information about an utterance's form and content, such as whether an utterance concerns the communication process itself or deals with the subject at hand. The kappa inter-annotator reliability scores for the rst test of DAMSL with human annotators show promise, but are on average 0.15 lower than the accepted kappa scores for such annotations. However, the slight revisions to DAMSL discussed here should increase accuracy on the next set of tests and produce a reliable, exible, and comprehensive utterance annotation scheme.
Can the Production Network Be the Testbed?

A persistent problem in computer network research is validation. When deciding how to evaluate a new feature or bug fix, a researcher or operator must trade-off realism (in terms of scale, actual user traffic, real equipment) and cost (larger scale costs more money, real user traffic likely requires downtime, and real equipment requires vendor adoption which can take years). Building a realistic testbed is hard because “real” networking takes place on closed, commercial switches and routers with special purpose hardware. But if we build our testbed from software switches, they run several orders of magnitude slower. Even if we build a realistic network testbed, it is hard to scale, because it is special purpose and is in addition to the regular network. It needs its own location, support and dedicated links. For a testbed to have global reach takes investment beyond the reach of most researchers. In this paper, we describe a way to build a testbed that is embedded in—and thus grows with—the network. The technique—embodied in our first prototype, FlowVisor—slices the network hardware by placing a layer between the control plane and the data plane. We demonstrate that FlowVisor slices our own production network, with legacy protocols running in their own protected slice, alongside experiments created by researchers. The basic idea is that if unmodified hardware supports some basic primitives (in our prototype, OpenFlow, but others are possible), then a worldwide testbed can ride on the coat-tails of deployments, at no extra expense. Further, we evaluate the performance impact and describe how FlowVisor is deployed at seven other campuses as part of a wider evaluation platform.
Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts

0020-0255/$ see front matter 2009  Corresponding author.  +82 2 958 5629. E-mail address: skee@kist.re.kr (S.-K. Park). This paper presents a novel vision-based global localization that uses hybrid maps of objects and spatial layouts. We model indoor environments with a stereo camera using the following visual cues: local invariant features for object recognition and their 3D positions for object pose estimation. We also use the depth information at the horizontal centerline of image where the optical axis passes through, which is similar to the data from a 2D laser range finder. This allows us to build our topological node that is composed of a horizontal depth map and an object location map. The horizontal depth map describes the explicit spatial layout of each local space and provides metric information to compute the spatial relationships between adjacent spaces, while the object location map contains the pose information of objects found in each local space and the visual features for object recognition. Based on this map representation, we suggest a coarse-to-fine strategy for global localization. The coarse pose is estimated by means of object recognition and SVDbased point cloud fitting, and then is refined by stochastic scan matching. Experimental results show that our approaches can be used for an effective vision-based map representation as well as for global localization methods.
A Survey of Surface-Based Illustrative Rendering for Visualization

In this paper, we survey illustrative rendering techniques for 3D surface models. We first discuss the field of illustrative visualization in general and provide a new definition for this sub-area of visualization. For the remainder of the survey, we then focus on surface-based models. We start by briefly summarizing the differential geometry fundamental to many approaches and discuss additional general requirements for the underlying models and the methods’ implementations. We then provide an overview of low-level illustrative rendering techniques including sparse lines, stippling and hatching, and illustrative shading, connecting each of them to practical examples of visualization applications. We also mention evaluation approaches and list various application fields, before we close with a discussion of the state of the art and future work.