nils-herrmann/clean_scirepeval huggingface dataset:
SATzilla: Portfolio-based Algorithm Selection for SAT

It has been widely observed that there is no single “dominant” SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use socalled empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.
Adapting the Locales Framework for Heuristic Evaluation of Groupware

Heuristic evaluation is a rapid, cheap and effective way for identifying usability problems in single user systems. However, current heuristics do not provide guidance for discovering problems specific to groupware usability. In this paper, we take the Locales Framework and restate it as heuristics appropriate for evaluating groupware. These are: 1) Provide locales; 2) Provide awareness within locales; 3) Allow individual views; 4) Allow people to manage and stay aware of their evolving interactions; and 5) Provide a way to organize and relate locales to one another. To see if these new heuristics are useful in practice, we used them to inspect the interface of Teamwave Workplace, a commercial groupware product. We were successful in identifying the strengths of Teamwave as well as both major and minor interface problems.
ENTREPRENEURIAL ACTION AND THE ROLE OF UNCERTAINTY IN THE THEORY OF THE ENTREPRENEUR

By considering the amount of uncertainty perceived and the willingness to bear uncertainty concomitantly, we provide a more complete conceptual model of entrepreneurial action that allows for examination of entrepreneurial action at the individual level of analysis while remaining consistent with a rich legacy of system-level theories of the entrepreneur. Our model not only exposes limitations of existing theories of entrepreneurial action but also contributes to a deeper understanding of important conceptual issues, such as the nature of opportunity and the potential for philosophical reconciliation among entrepreneurship scholars.
Accurate vehicle self-localization in high definition map dataset

One of the biggest challenges in automated driving is the ability to determine the vehicleâĂŹs location in realtime - a process known as self-localization or ego-localization. An automated driving system must be reliable under harsh conditions and environmental uncertainties (e.g. GPS denial or imprecision), sensor malfunction, road occlusions, poor lighting, and inclement weather. To cope with this myriad of potential problems, systems typically consist of a GPS receiver, in-vehicle sensors (e.g. cameras and LiDAR devices), and 3D High-Definition (3D HD) Maps. In this paper, we review state-of-the-art self-localization techniques, and present a benchmark for the task of image-based vehicle self-localization. Our dataset was collected on 10km of the Warren Freeway in the San Francisco Area under reasonable traffic and weather conditions. As input to the localization process, we provide timestamp-synchronized, consumer-grade monocular video frames (with camera intrinsic parameters), consumer-grade GPS trajectory, and production-grade 3D HD Maps. For evaluation, we provide survey-grade GPS trajectory. The goal of this dataset is to standardize and formalize the challenge of accurate vehicle self-localization and provide a benchmark to develop and evaluate algorithms.
Nonlinear Attitude Filtering Methods

This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST, the super-iterated extended Kalman filter, the interlaced extended Kalman filter, and the second-order Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A two-step approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed, including particle filters and a Bayesian filter based on a non-Gaussian, finite-parameter probability density function on SO(3) . Finally, the predictive filter, nonlinear observers and adaptive approaches are shown. The strengths and weaknesses of the various approaches are discussed.
Design Issues, Characteristics and Challenges in Routing Protocols for Wireless Sensor Networks

The modern research has found a variety of applications and systems with vastly varying requirements and characteristics in Wireless Sensor Networks (WSNs). The research has led to materialization of many application specific routing protocols which must be energy-efficient. As a consequence, it is becoming increasingly difficult to discuss the design issues requirements regarding hardware and software support. Implementation of efficient system in a multidisciplinary research such as WSNs is becoming very difficult. In this paper we discuss the design issues in routing protocols for WSNs by considering its various dimensions and metrics such as QoS requirement, path redundancy etc. The paper concludes by presenting
Fitness functions for searching the Mandelbrot set

The Mandelbrot set is a famous fractal. It serves as the source of a large number of complex mathematical images. Evolutionary computation can be used to search the Mandelbrot set for interesting views. This study compares the results of using several different fitness functions for this search. Some of the fitness functions give substantial control over the appearance of the resulting views while others simply locate parts of the Mandelbrot set in which there are complicated structures. All of the fitness functions are based on finding desirable patterns in the number of iterations of the basic Mandelbrot formula to diverge on a set of points arranged in a regular grid near the boundary of the set. It is shown that using different fitness functions causes an evolutionary algorithm to locate difference types of views into the Mandelbrot set.
Exome sequencing identifies NBEAL2 as the causative gene for Gray Platelet Syndrome

Gray platelet syndrome (GPS) is a predominantly recessive platelet disorder that is characterized by mild thrombocytopenia with large platelets and a paucity of α-granules; these abnormalities cause mostly moderate but in rare cases severe bleeding. We sequenced the exomes of four unrelated individuals and identified NBEAL2 as the causative gene; it has no previously known function but is a member of a gene family that is involved in granule development. Silencing of nbeal2 in zebrafish abrogated thrombocyte formation.
Sparse Matrix-Vector Multiplication on GPGPUs

The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrix-vector multiplication is therefore crucial and has been the subject of an immense amount of research, with interest renewed with every major new trend in high-performance computing architectures. The introduction of General-Purpose Graphics Processing Units (GPGPUs) is no exception, and many articles have been devoted to this problem.
 With this article, we provide a review of the techniques for implementing the SpMV kernel on GPGPUs that have appeared in the literature of the last few years. We discuss the issues and tradeoffs that have been encountered by the various researchers, and a list of solutions, organized in categories according to common features. We also provide a performance comparison across different GPGPU models and on a set of test matrices coming from various application domains.
Evaluation of Android Dalvik virtual machine

More than half of the smart phones world-wide are currently employing the Android platform, which employs Java for programming its applications. The Android Java is to be executed by the Dalvik virtual machine (VM), which is quite different from the traditional Java VM such as Oracle's HotSpot VM. That is, Dalvik employs register-based bytecode while HotSpot employs stack-based bytecode, requiring a different way of interpretation. Also, Dalvik uses trace-based just-in-time compilation (JITC), while HotSpot uses method-based JITC. Therefore, it is questioned how the Dalvik VM performs compared the HotSpot VM. Unfortunately, there has been little comparative evaluation of both VMs, so the performance of the Dalvik VM is not well understood. More importantly, it is also not well understood how the performance of the Dalvik VM affects the overall performance of the Android applications (apps). In this paper, we make an attempt to evaluate the Dalvik VM. We install both VMs on the same board and compare the performance using EEMBC benchmark. Our results show that Dalvik slightly outperforms HotSpot in the interpreter mode due to its register-based bytecode. In the JITC mode, however, Dakvik is slower than HotSpot by more than 2.9 times and its generated code size is not smaller than HotSpot's due to its worse code quality and trace-chaining code. We also investigated how real Android apps are different from Java benchmarks, to understand why the slow Dalvik VM does not affect the performance of the Android apps seriously.
Semantic Segmentation without Annotating Segments

Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that object bounding boxes are provided by object detectors, but no training data with annotated segments are available. Based on a set of segment hypotheses, we introduce a simple voting scheme to estimate shape guidance for each bounding box. The derived shape guidance is used in the subsequent graph-cut-based figure-ground segmentation. The final segmentation result is obtained by merging the segmentation results in the bounding boxes. We conduct an extensive analysis of the effect of object bounding box accuracy. Comprehensive experiments on both the challenging PASCAL VOC object segmentation dataset and GrabCut-50 image segmentation dataset show that the proposed approach achieves competitive results compared to previous detection or bounding box prior based methods, as well as other state-of-the-art semantic segmentation methods.
Identifying Personal Stories in Millions of Weblog Entries

Stories of people's everyday experiences have long been the focus of psychology and sociology research, and are increasingly being used in innovative knowledge-based technologies. However, continued research in this area is hindered by the lack of standard corpora of sufficient size and by the costs of creating one from scratch. In this paper, we describe our efforts to develop a standard corpus for researchers in this area by identifying personal stories in the tens of millions of blog posts in the ICWSM 2009 Spinn3r Dataset. Our approach was to employ statistical text classification technology on the content of blog entries, which required the creation of a sufficiently large set of annotated training examples. We describe the development and evaluation of this classification technology and how it was applied to the dataset in order to identify nearly a million
Novel Sum-of-Sinusoids Simulation Models for Rayleigh and Rician Fading Channels

The statistical properties of Clarke's fading model with a finite number of sinusoids are analyzed, and an improved reference model is proposed for the simulation of Rayleigh fading channels. A novel statistical simulation model for Rician fading channels is examined. The new Rician fading simulation model employs a zero-mean stochastic sinusoid as the specular (line-of-sight) component, in contrast to existing Rician fading simulators that utilize a non-zero deterministic specular component. The statistical properties of the proposed Rician fading simulation model are analyzed in detail. It is shown that the probability density function of the Rician fading phase is not only independent of time but also uniformly distributed over [-pi, pi). This property is different from that of existing Rician fading simulators. The statistical properties of the new simulators are confirmed by extensive simulation results, showing good agreement with theoretical analysis in all cases. An explicit formula for the level-crossing rate is derived for general Rician fading when the specular component has non-zero Doppler frequency
Staging for vulvar cancer.

Vulvar cancer has been staged by the International Federation of Gynaecology and Obstetrics (FIGO) since 1969, and the original staging system was based on clinical findings only. This system provided a very good spread of prognostic groupings. Because vulvar cancer is virtually always treated surgically, the status of the lymph nodes is the most important prognostic factor and this can only be determined with certainty by histological examination of resected lymph nodes, FIGO introduced a surgical staging system in 1988. This was modified in 1994 to include a category of microinvasive vulvar cancer (stage IA), because such patients have virtually no risk of lymph node metastases. This system did not give a reasonably even spread of prognostic groupings. In addition, patients with stage III disease were shown to be a heterogeneous group prognostically, and the number of positive nodes and the morphology of those nodes were not taken into account. A new surgical staging system for vulvar cancer was introduced by FIGO in 2009. Initial retrospective analyses have suggested that this new staging system has overcome the major deficiencies in the 1994 system.
Life Threatening Complications Post-Liposuction

Liposuction is one of the most commonly performed aesthetic procedures. It is performed worldwide as an outpatient procedure. However, the complications are underestimated and underreported by caregivers. We present a case of delayed diagnosis of bilothorax secondary to liver and gallbladder injury after tumescent liposuction. A 26-year-old female patient was transferred to our emergency department from an aesthetic clinic with worsening dyspnea, tachypnea and fatigue. She had undergone extensive liposuction of the thighs, buttocks, back and abdomen 5 days prior to presentation. A chest X-ray showed significant right-sided pleural effusion. Thoracentesis was performed and drained bilious fluid. CT scan of the abdomen revealed pleural, liver and gall bladder injury. An exploratory laparoscopy confirmed the findings, the collections were drained; cholecystectomy and intraoperative cholangiogram were performed. The patient did very well postoperatively and was discharged home in 2 days. Even though liposuction is considered a simple office-based procedure, its complications can be fatal. The lack of strict laws that exclusively place this procedure in the hands of medical professionals allow these procedures to still be done by less experienced hands and in outpatient-based settings. Our case serves to highlight yet another unique but potentially fatal complication of liposuction. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
A Data Mining Framework for Building Intrusion Detection Models

There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgraded computing environments. Since many current IDSs are constructed by manual encoding of expert security knowledge, changes to IDSs are expensive and slow. In this paper, we describe a data mining framework for adaptively building Intrusion Detection (ID) models. The central idea is to utilize auditing programs to extract an extensive set of features that describe each network connection or host session, and apply data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities. These rules can then be used for misuse detection and anomaly detection. Detection models for new intrusions or specific components of a network system are incorporated into an existing IDS through a meta-learning (or co-operative learning) process, which produces a meta detection model that combines evidence from multiple models. We discuss the strengths of our data mining programs, namely, classification, meta-learning, association rules, and frequent episodes. We report our results of applying these programs to the (extensively gathered) network audit data from the DARPA Intrusion Detection Evaluation Program.
Suicide announcement on Facebook.

The media and the Internet may be having an influence on suicidal behavior. Online social networks such as Facebook represent a new facet of global information transfer. The impact of these online social networks on suicidal behavior has not yet been evaluated.



To discuss potential effects of suicide notes on Facebook on suicide prevention and copycat suicides, and to create awareness among health care professionals.



We present a case involving a suicide note on Facebook and discuss potential consequences of this phenomenon based on literature found searching PubMed and Google.



There are numerous reports of suicide notes on Facebook in the popular press, but none in the professional literature. Online social network users attempted to prevent planned suicides in several reported cases. To date there is no documented evidence of a copycat suicide, directly emulating a suicide announced on Facebook.



Suicide notes on online social networks may allow for suicide prevention via the immediate intervention of other network users. But it is not yet clear to what extent suicide notes on online social networks actually induce copycat suicides. These effects deserve future evaluation and research.
Relief texture mapping

We present an extension to texture mapping that supports the representation of 3-D surface details and view motion parallax. The results are correct for viewpoints that are static or moving, far away or nearby. Our approach is very simple: a relief texture (texture extended with an orthogonal displacement per texel) is mapped onto a polygon using a two-step process: First, it is converted into an ordinary texture using a surprisingly simple 1-D forward transform. The resulting texture is then mapped onto the polygon using standard texture mapping. The 1-D warping functions work in texture coordinates to handle the parallax and visibility changes that result from the 3-D shape of the displacement surface. The subsequent texture-mapping operation handles the transformation from texture to screen coordinates.
Pattern-Based Classification: A Unifying Perspective

The use of patterns in predictive models is a topic that has received a lot of attention in recent years. Pattern mining can help to obtain models for structured domains, such as graphs and sequences, and has been proposed as a means to obtain more accurate and more interpretable models. Despite the large amount of publications devoted to this topic, we believe however that an overview of what has been accomplished in this area is missing. This paper presents our perspective on this evolving area. We identify the principles of pattern mining that are important when mining patterns for models and provide an overview of pattern-based classification methods. We categorize these methods along the following dimensions: (1) whether they post-process a pre-computed set of patterns or iteratively execute pattern mining algorithms; (2) whether they select patterns model-independently or whether the pattern selection is guided by a model. We summarize the results that have been obtained for each of these methods.
Designing Large Scale REST APIs Based on REST Chart

REST Chart is a Petri-Net based XML modeling framework for REST API. This paper presents two important enhancements and extensions to REST Chart modeling - Hyperlink Decoration and Hierarchical REST Chart. In particular, the proposed Hyperlink Decoration decomposes resource connections from resource representation, such that hyperlinks can be defined independently of schemas. This allows a Navigation-First Design by which the important global connections of a REST API can be designed first and reused before the local resource representations are implemented and specified. Hierarchical REST Chart is a powerful mechanism to rapidly decompose and extend a REST API in several dimensions based on Hyperlink Decoration. These new mechanisms can be used to manage the complexities in large scale REST APIs that undergo frequent changes as in some large scale open source development projects. This paper shows that these new capabilities can fit nicely in the REST Chart XML with very minor syntax changes. These enhancements to REST Chart are applied successfully in designing and verifying REST APIs for software-defined-networking (SDN) and Cloud computing.