nils-herrmann/clean_scirepeval huggingface dataset:
A Compact Formula for the Derivative of a 3-D Rotation in Exponential Coordinates

We present a compact formula for the derivative of a 3-D rotation matrix with respect to its exponential coordinates. A geometric interpretation of the resulting expression is provided, as well as its agreement with other less-compact but better-known formulas. To the best of our knowledge, this simpler formula does not appear anywhere in the literature. We hope by providing this more compact expression to alleviate the common pressure to reluctantly resort to alternative representations in various computational applications simply as a means to avoid the complexity of differential analysis in exponential coordinates.
Visual Sedimentation

We introduce Visual Sedimentation, a novel design metaphor for visualizing data streams directly inspired by the physical process of sedimentation. Visualizing data streams (e. g., Tweets, RSS, Emails) is challenging as incoming data arrive at unpredictable rates and have to remain readable. For data streams, clearly expressing chronological order while avoiding clutter, and keeping aging data visible, are important. The metaphor is drawn from the real-world sedimentation processes: objects fall due to gravity, and aggregate into strata over time. Inspired by this metaphor, data is visually depicted as falling objects using a force model to land on a surface, aggregating into strata over time. In this paper, we discuss how this metaphor addresses the specific challenge of smoothing the transition between incoming and aging data. We describe the metaphor's design space, a toolkit developed to facilitate its implementation, and example applications to a range of case studies. We then explore the generative capabilities of the design space through our toolkit. We finally illustrate creative extensions of the metaphor when applied to real streams of data.
Energy production from biomass (Part 3): Gasification technologies.

The conversion of biomass by gasification into a fuel suitable for use in a gas engine increases greatly the potential usefulness of biomass as a renewable resource. Gasification is a robust proven technology that can be operated either as a simple, low technology system based on a fixed-bed gasifier, or as a more sophisticated system using fluidized-bed technology. The properties of the biomass feedstock and its preparation are key design parameters when selecting the gasifier system. Electricity generation using a gas engine operating on gas produced by the gasification of biomass is applicable equally to both the developed world (as a means of reducing greenhouse gas emissions by replacing fossil fuel) and to the developing world (by providing electricity in rural areas derived from traditional biomass).
Outcome of thoracolumbar burst fractures treated with indirect reduction and fixation without fusion

Indirect reduction and fixation is not a new method in the treatment of thoracolumbar burst fractures but the indications and efficacy are controversial. The current study was designed to evaluate the efficacy of indirect reduction without fusion. Sixty-four patients with single-level thoracolumbar burst fractures were identified and treated by this method. The outcome was analyzed by the Frankel method, radiographic measurements, and at the latest follow-up the Denis Pain Scale and Oswestry disability index (ODI) were used to assess back pain and functional outcome. The average follow-up period was 40.1 months. The anterior vertebral height (AVH) was corrected from 55.2 to 97.2% post-operatively and decreased to 88.9% after hardware removal. The posterior vertebral height (PVH) increased from 88.9 to 99.1% post-operatively and decreased slightly after implant removal to 93.7%. The average pre-operative canal compromise was 41.4%, which decreased to 13.7% at last follow-up. Except for three paraplegic patients, neurological status significantly improved or stayed normal in the study’s remaining 61 patients. Fifty-two of sixty-four patients had excellent or good function. At latest follow-up the average ODI score was 16.7 and the Denis pain score improved in all patients but one. Indirect reduction and fixation can not only restore vertebral column structure but also, more importantly, patients’ functional outcome.
Structured Literature Image Finder: Extracting Information from Text and Images in Biomedical Literature

Slif uses a combination of text-mining and image processing to extract information from figures in the biomedical literature. It also uses innovative extensions to traditional latent topic modeling to provide new ways to traverse the literature. Slif provides a publicly available searchable database (http://slif.cbi.cmu.edu). Slif originally focused on fluorescence microscopy images. We have now extended it to classify panels into more image types. We also improved the classification into subcellular classes by building a more representative training set. To get the most out of the human labeling effort, we used active learning to select images to label. We developed models that take into account the structure of the document (with panels inside figures inside papers) and the multi-modality of the information (free and annotated text, images, information from external databases). This has allowed us to provide new ways to navigate a large collection of documents.
Computational Poetry Workshop: Making Sense of Work in Progress

Creativity cannot exist in a vacuum; it develops through feedback, learning, reflection and social interaction with others. However, this perspective has been relatively under-investigated in computational creativity research, which typically examines systems that operate individually. We develop a thought experiment showing how structured dialogues can help develop the creative aspects of computer poetry. Centrally in this approach, we ask questions of a poem, inviting it to tell us in what way it may be considered a “creative making.”
Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers

A major challenge in real-world feature matching problems is to tolerate the numerous outliers arising in typical visual tasks. Variations in object appearance, shape, and structure within the same object class make it harder to distinguish inliers from outliers due to clutters. In this paper, we propose a max-pooling approach to graph matching, which is not only resilient to deformations but also remarkably tolerant to outliers. The proposed algorithm evaluates each candidate match using its most promising neighbors, and gradually propagates the corresponding scores to update the neighbors. As final output, it assigns a reliable score to each match together with its supporting neighbors, thus providing contextual information for further verification. We demonstrate the robustness and utility of our method with synthetic and real image experiments.
A biologically inspired robot for lunar In-Situ Resource Utilization

Successful long-term settlements on the Moon will need a supply of resources such as oxygen and water, yet the process of regularly transporting these resources from Earth would be prohibitively costly and dangerous. One alternative would be an approach using heterogeneous, autonomous robotic teams, which could collect and extract these resources from the surrounding environment (In-Situ Resource Utilization). The Whegs™ robotic platform, with its demonstrated capability to negotiate obstacles and traverse irregular terrain, is a good candidate for a lunar rover concept. In this research, Lunar Whegs™ is constructed as a proof-of-concept rover that would be able to navigate the surface of the moon, collect a quantity of regolith, and transport it back to a central processing station. The robot incorporates an actuated scoop, specialized feet for locomotion on loose substrates, Light Detection and Ranging (LIDAR) obstacle sensing and avoidance, and sealing and durability features for operation in an abrasive environment.
Optimal Myopic Sensing and Dynamic Spectrum Access in Cognitive Radio Networks with Low-Complexity Implementations

Cognitive radio techniques allow secondary users (SU's) to opportunistically access underutilized primary channels that are licensed to primary users. We consider a group of SU's with limited spectrum sensing capabilities working cooperatively to find primary channel spectrum holes. The objective is to design the optimal sensing and access policies that maximize the total secondary throughput on primary channels accrued over time. Although the problem can be formulated as a Partially Observable Markov Decision Process (POMDP), the optimal solutions are intractable. Instead, we find the optimal sensing policy within the class of myopic policies. Compared to other existing approaches, our policy is more realistic because it explicitly assigns SU's to sense specific primary channels by taking into account spatial and temporal variations of primary channels.  (1) formulation of a centralized spectrum sensing/access architecture that allows exploitation of all available primary spectrum holes; and (2) proposing sub-optimal myopic sensing policies with low-complexity implementations and performance close to the myopic policy. We show that our proposed sensing/access policy is close to the optimal POMDP solution and outperforms other proposed strategies. We also propose a Hidden Markov Model based algorithm to estimate the parameters of primary channel Markov models with a linear complexity.
Informant reports : A cheap , fast , and easy method for personality assessment

Despite widespread agreement that multi-method assessments are optimal in personality research, the literature is dominated by a single method: self-reports. This pattern seems to be based, at least in part, on widely held preconceptions about the costs of non-self-report methods, such as informant methods. Researchers seem to believe that informant methods are: (a) time-consuming, (b) expensive, (c) ineffective (i.e., informants will not cooperate), and (d) particularly vulnerable to faking or invalid responses. This article evaluates the validity of these preconceptions in light of recent advances in Internet technology, and proposes some strategies for making informant methods more effective. Drawing on data from three separate studies, I demonstrate that, using these strategies, informant reports can be collected with minimal effort and few monetary costs. In addition, informants are generally very willing to cooperate (e.g., response rates of 76–95%) and provide valid data (in terms of strong consensus and self-other agreement). Informant reports represent a mostly untapped resource that researchers can use to improve the validity of personality assessments and to address new questions that cannot be examined with self-reports alone.
Analysis of Windings in Variable Reluctance Resolver

In this paper, the relationship between the numbers of stator slots, winding polarities, and rotor poles for variable reluctance resolvers is derived and verified, which makes it possible for the same stator and winding to be shared by the rotors with different poles. Based on the established relationship, a simple factor is introduced to evaluate the level of voltage harmonics as an index for choosing appropriate stator slot and rotor pole combinations. With due account for easy manufacturing, alternate windings are proposed without apparent deterioration in voltage harmonics of a resolver. In particular, alternate windings with nonoverlapping uniform coils are proved to be possible for output windings in some stator slot and rotor pole combinations, which further simplify the manufacture process. Finite element method is adopted to verify the proposed design, together with experiments on the prototypes.
Factors Affecting Trust in Market

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Automatic generation of system test cases from use case specifications

In safety critical domains, system test cases are often derived from functional requirements in natural language (NL) and traceability between requirements and their corresponding test cases is usually mandatory. The definition of test cases is therefore time-consuming and error prone, especially so given the quickly rising complexity of embedded systems in many critical domains. Though considerable research has been devoted to automatic generation of system test cases from NL requirements, most of the proposed approaches re- quire significant manual intervention or additional, complex behavioral modelling. This significantly hinders their applicability in practice. In this paper, we propose Use Case Modelling for System Tests Generation (UMTG), an approach that automatically generates executable system test cases from use case spec- ifications and a domain model, the latter including a class diagram and constraints. Our rationale and motivation are that, in many environments, including that of our industry partner in the reported case study, both use case specifica- tions and domain modelling are common and accepted prac- tice, whereas behavioural modelling is considered a difficult and expensive exercise if it is to be complete and precise. In order to extract behavioral information from use cases and enable test automation, UMTG employs Natural Language Processing (NLP), a restricted form of use case specifica- tions, and constraint solving.
Courses : Innovation in Education ?

Researchers have extensively chronicled the trends and challenges in higher education (Altbach et al. 2009). MOOCs appear to be as much about the collective grasping of universities’ leaders to bring higher education into the digital age as they are about a particular method of teaching. In this chapter, I won’t spend time commenting on the role of MOOCs in educational transformation or even why attention to this mode of delivering education has received unprecedented hype (rarely has higher education as a system responded as rapidly to a trend as it has responded to open online courses). Instead, this chapter details different MOOC models and the underlying pedagogy of each.
The face-time continuum: lifespan changes in facial width-to-height ratio impact aging-associated perceptions.

Aging influences how a person is perceived on multiple dimensions (e.g., physical power). Here we examined how facial structure informs these evolving social perceptions. Recent work examining young adults' faces has revealed the impact of the facial width-to-height ratio (fWHR) on perceived traits, such that individuals with taller, thinner faces are perceived to be less aggressive, less physically powerful, and friendlier. These perceptions are similar to those stereotypically associated with older adults. Examining whether fWHR might contribute to these changing perceptions over the life span, we found that age provides a shifting context through which fWHR differentially impacts aging-related social perceptions (Study 1). In addition, archival analyses (Study 2) established that fWHR decreases across age, and a subsequent study found that fWHR mediated the relationship between target age and multiple aging-related perceptions (Study 3). The findings provide evidence that fWHR decreases across age and influences stereotypical perceptions that change with age.
Addressing cloud computing security issues

The recent emergence of Cloud Computing has drastically altered everyone’s perception of infrastructure architectures, software delivery and development models. Projecting as an evolutionary step, following the transition from mainframe computers to client/server deployment models, cloud computing encompasses elements from grid computing, utility computing and autonomic computing, into an innovative deployment architecture. This rapid transition towards the clouds, has fuelled concerns on a critical issue for the success of information systems, communication and information security. From a security perspective, a number of unchartered risks and challenges have been introduced from this relocation to the clouds, deteriorating much of the effectiveness of traditional protection mechanisms. As a result the aim of this paper is twofold; firstly to evaluate cloud security by identifying unique security requirements and secondly to attempt to present a viable solution that eliminates these potential threats. This paper proposes introducing a Trusted Third Party, tasked with assuring specific security characteristics within a cloud environment. The proposed solution calls upon cryptography, specifically Public Key Infrastructure operating in concert with SSO and LDAP, to ensure the authentication, integrity and confidentiality of involved data and communications. The solution, presents a horizontal level of service, available to all implicated entities, that realizes a security mesh, within which essential trust is maintained.
An Empirical Examination of Users’ Information Hiding in a Crowdfunding Context

Online privacy remains an ongoing source of debate in society. Sensitive to this, many web platforms are offering users greater, more granular control over how and when their information is revealed. However, recent research suggests that information control mechanisms of this sort are not necessarily of economic benefit to the parties involved. We examine the use of these mechanisms and their economic consequences, leveraging data from one of the world's largest global crowdfunding platforms, where contributors can conceal their identity or contribution amounts from public display. We find that information hiding is more likely when contributors are under greater scrutiny or exhibiting “undesirable” behavior. We also identify an anchoring effect from prior contributions, which is eliminated when earlier contributors conceal their amounts. Subsequent analyses indicate that a nuanced approach to the design and provision of information control mechanisms, such as varying default settings based on contribution amounts, can help promote larger contributions.
Improving Text Retrieval Precision and Answer Accuracy in Question Answering Systems

Answering (QA) systems are often built modularly, with a text retrieval component feeding forward into an answer extraction component. Conventional wisdom suggests that, the higher the quality of the retrieval results used as input to the answer extraction module, the better the extracted answers, and hence system accuracy, will be. This turns out to be a poor assumption, because text retrieval and answer extraction are tightly coupled. Improvements in retrieval quality can be lost at the answer extraction module, which can not necessarily recognize the additional answer candidates provided by improved retrieval. Going forward, to improve accuracy on the QA task, systems will need greater coordination between text retrieval and answer extraction modules.
Host-gut microbiota metabolic interactions.

The composition and activity of the gut microbiota codevelop with the host from birth and is subject to a complex interplay that depends on the host genome, nutrition, and life-style. The gut microbiota is involved in the regulation of multiple host metabolic pathways, giving rise to interactive host-microbiota metabolic, signaling, and immune-inflammatory axes that physiologically connect the gut, liver, muscle, and brain. A deeper understanding of these axes is a prerequisite for optimizing therapeutic strategies to manipulate the gut microbiota to combat disease and improve health.
Combined coverage path planning for autonomous cleaning robots in unstructured environments

Complete coverage path planning is a key problem for autonomous cleaning robots, which concerns not only the cleaning efficiency but also the adaptability to unstructured environments. But the diversity of environments and limited perception ability of the robot make the problem still unsolved. In this paper, a novel strategy of combined coverage path planning is proposed, which combines the random path planning and local complete coverage path planning. The random planning lets the robot run straight until an obstacle is encountered. After turning a random angle, the robot continues the straight run. This mode is easy to implement and can provide the robot with the flexibility to environments. And local complete coverage path planning works out a comb-like path depending on dead reckoning. The comb-like path can cover every part in a relative small area. All these functions are just based on general hardware: ultrasonic sensors, infrared sensors, incremental encoders, DC motors, vacuum, etc. Finally the experiment shows that this strategy can work efficiently and robustly in common family environments.
Big Data on Real-World Applications. Chapter 3: Introduction to Big Data in Education and Its Contribution to the Quality Improvement Processes

In this chapter, we introduce the readers to the field of big educational data and how big educational data can be analysed to provide insights into different stakeholders and thereby foster data driven actions concerning quality improvement in education. For the analysis and exploitation of big educational data, we present different techniques and popular applied scientific methods for data analysis and manipulation such as analyt‐ ics and different analytical approaches such as learning, academic and visual analytics, providing examples of how these techniques and methods could be used. The concept of quality improvement in education is presented in relation to two factors: (a) to improve‐ ment science and its impact on different processes in education such as the learning, educational and academic processes and (b) as a result of the practical application and realization of the presented analytical concepts. The context of health professions education is used to exemplify the different concepts.
Organizational Readiness for Successful Knowledge Sharing: Challenges for Public Sector Managers

This paper contributes to our understanding of an increasingly important practical problem, namely the effectiveness of knowledge management in organizations. As with many other managerial innovations, knowledge management appears to have been adopted firstly by manufacturing firms, and is only now beginning to permeate the service sector, predominantly in professional services such as consulting (Hansen et al., 1999;  1999). Public services, traditionally slower to embrace innovative management practices, are only beginning to recognize the importance of knowledge management. There is, as yet, little published research of its implementation in this context (Bate & Robert, 2002).