nils-herrmann/clean_scirepeval huggingface dataset:
Effects of prior hamstring strain injury on strength, flexibility, and running mechanics.

Previous studies have shown evidence of residual scar tissue at the musculotendon junction following a hamstring strain injury, which could influence re-injury risk. The purpose of this study was to investigate whether bilateral differences in strength, neuromuscular patterns, and musculotendon kinematics during sprinting are present in individuals with a history of unilateral hamstring injury, and whether such differences are linked to the presence of scar tissue.



Eighteen subjects with a previous hamstring injury (>5 months prior) participated in a magnetic resonance (MR) imaging exam, isokinetic strength testing, and a biomechanical assessment of treadmill sprinting. Bilateral comparisons were made for peak knee flexion torque, angle of peak torque, and the hamstrings:quadriceps strength ratio, as well as muscle activations and peak hamstring stretch during sprinting. MR images were used to measure the volumes of the proximal tendon/aponeurosis of the biceps femoris, with asymmetries considered indicative of scar tissue.



A significantly enlarged proximal biceps femoris tendon volume was measured on the side of prior injury. However, no significant differences between the previously injured and uninjured limbs were found in strength measures, peak hamstring stretch, or muscle activation patterns. Further, the degree of asymmetry in tendon volume was not correlated to any of the functional measures.



Injury-induced changes in morphology do not seem discernable from strength measures, running kinematics, or muscle activation patterns. Further research is warranted to ascertain whether residual scarring alters localized musculotendon mechanics in a way that may contribute to the high rates of muscle re-injury that are observed clinically.
Task driven perceptual organization for extraction of rooftop polygons

A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature veri cation is used so that features, and links between the features, are veri ed with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the \best" grouping of features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the nal set of roof
Automatic generation of technical documentation

Natural-language generation (NLG) techniques can be used to automatically produce technical documentation from a domain knowledge base and linguistic and contextual models. We discuss this application of NLG technology from both a technical and a usefulness (costs and benefits) perspective. This discussion is based largely on our experiences with the idas documentation-generation project, and the reactions various interested people from industry have had to idas. We hope that this summary of our experiences with idas and the lessons we have learned from it will be beneficial for other researchers who wish to build technical-documentation generation systems. This paper will appear in Applied Artificial Intelligence, volume 9 (1995). Now at CoGenTex, Inc, 840 Hanshaw Rd, Ithaca, NY 14850 USA. Email is ehud@cogentex.com.
SpongeFiles: mitigating data skew in mapreduce using distributed memory

Data skew is a major problem for data processing platforms like MapReduce. Skew causes worker tasks to spill to disk what they cannot fit in memory, which slows down the task and the overall job. Moreover, performance of other jobs sharing same disk degrades. In many cases, this situation occurs even as the cluster has plenty of spare memory it is just not used evenly. We introduce SpongeFiles, a novel distributed-memory abstraction tailored to data processing environments like MapReduce. A SpongeFile is a logical byte array, comprised of large chunks that can be stored in a variety of locations in the cluster. Spilled data goes to SpongeFiles, which route it to the nearest location with sufficient capacity (local memory, remote memory, local disk, or remote disk as a last resort). By enabling memory-sapped nodes to tap into the spare capacity of their neighbors, SpongeFiles minimize expensive disk spilling, thereby improving performance. In our experiments with Hadoop and Pig, SpongeFiles reduce overall job runtimes by up to 55% and by up to 85% under disk contention.
On Radio Access Network Slicing from a Radio Resource Management Perspective

Network slicing is a fundamental capability for future 5G networks to properly support current and envisioned future application scenarios. Network slicing facilitates a cost-effective deployment and operation of multiple logical networks over a common physical network infrastructure such that each network is customized to best serve the needs of specific applications (e.g., mobile broadband, Internet of Things applications) and/or communications service providers (e.g., special purpose service providers for different sectors such as public safety, utilities, smart city, and automobiles). Slicing a RAN becomes particularly challenging due to the inherently shared nature of the radio channel and the potential influence that any transmitter may have on any receiver. In this respect, this article analyzes the RAN slicing problem in a multi-cell network in relation to the RRM functionalities that can be used as a support for splitting the radio resources among the RAN slices. Four different RAN slicing approaches are presented and compared from different perspectives, such as the granularity in the assignment of radio resources and the degrees of isolation and customization.
PID control scheme for twin rotor MIMO system using a real valued genetic algorithm with a predetermined search range

This paper discusses the proportional-integral-derivative control scheme for a twin rotor multiple input multiple output system (TRMS), which is a nonlinear system with two degrees of freedom and cross couplings. The objectives of the work are to stabilize the TRMS, to attain a particular position and to make it to track a trajectory. Four PID controllers are used for achieving the objectives. The parameters of the PID controllers are tuned using the real valued genetic algorithm (RGA). The initial search range of the RGA is obtained using a control design optimization (CDO) technique. The control scheme is initially implemented for the decoupled system viz., horizontal and vertical 1-degree of freedom (1-DOF) systems, using 1 PID controller for each system, and it is followed by the implementation of the control scheme for the 2-degree of freedom (2-DOF) System.
Dimensions of Abusive Language on Twitter

In this paper, we use a new categorical form of multidimensional register analysis to identify the main dimensions of functional linguistic variation in a corpus of abusive language, consisting of racist and sexist Tweets. By analysing the use of a wide variety of parts-ofspeech and grammatical constructions, as well as various features related to Twitter and computer-mediated communication, we discover three dimensions of linguistic variation in this corpus, which we interpret as being related to the degree of interactive, antagonistic and attitudinal language exhibited by individual Tweets. We then demonstrate that there is a significant functional difference between racist and sexist Tweets, with sexists Tweets tending to be more interactive and attitudinal than racist Tweets.
Managing Organizational Knowledge By Diagnosing Intellectual Capital : Framing and Advancing the State of the Field

Since organizational knowledge is at the crux of sustainable competitive advantage, the burgeoning field of intellectual capital is an exciting area for both researchers and practitioners. Intellectual capital is conceptualized from numerous disciplines making the field a mosaic of perspectives. Accountants are interested in how to measure it on the balance sheet, information technologists want to codify it on systems, sociologists want to balance power with it, psychologists want to develop minds because of it, human resource managers want to calculate an ROI on it, and training and development officers want to make sure that they can build it. The following article represents a comprehensive literature review from a variety of managerial disciplines. In addition to highlighting the research to date, avenues for future pursuit are also offered.
Partial face detection for continuous authentication

In this paper, a part-based technique for real time detection of users' faces on mobile devices is proposed. This method is specifically designed for detecting partially cropped and occluded faces captured using a smartphone's front-facing camera for continuous authentication. The key idea is to detect facial segments in the frame and cluster the results to obtain the region which is most likely to contain a face. Extensive experimentation on a mobile dataset of 50 users shows that our method performs better than many state-of-the-art face detection methods in terms of accuracy and processing speed.
Building a corpus of "real" texts for deception detection

Text-based deception detection is currently emerging as a vital multidisciplinary field due to its indisputable theoretical and practical value (police, security, and customs, including predatory communications, such as Internet scams). A very important issue associated with deception detection is designing valid text corpora. Most research has been carried out using texts produced in laboratory settings. There is a lack of "real" deceptive texts written when the stakes for deception are high as they are obviously difficult to collect and label. In addition, studies in text-based deception detection have mostly been performed for Romance and Germanic languages. There are few studies dealing with deception detection in Slavic languages. In this paper one can find an overview of available text corpora used for studying text-based deception detection as well as the description of how the first corpus of "real" deceptive texts for Slavic languages was collected and labeled. We expect this corpus once finished to be a handy tool for developing and testing new deception detection techniques and for carrying out related cross-cultural studies.
ANALYSIS OF SEGMENTATION PARAMETERS IN ECOGNITION SOFTWARE USING HIGH RESOLUTION QUICKBIRD MS IMAGERY

For objectoriented classification approaches, main step is the segmentation part of the imagery. In eCognition v.4.0.6 software, with the segmentation process, meaningful objects can be created for following steps. In the experimental imagery with 2.4m ground sampling distance (GSD) has been used and several different parameters e.g. scale, color/shape and smoothness/compactness parameters have been tested accordingly. Additionally, segmentation parameters were set to low and high values and thus, dissimilarity of segmentation results were examined.
Power Cycle Testing of Power Switches: A Literature Survey

Reliability of power converters and lifetime prediction has been a major topic of research in the last few decades, especially for traction applications. The main failures in high power semiconductors are caused by thermomechanical fatigue. Power cycling and temperature cycling are the two most common thermal acceleration tests used in assessing reliability. The objective of this paper is to study the various power cycling tests found in the literature and to develop generalized steps in planning application specific power cycling tests. A comparison of different tests based on the failures, duration, test circuits, and monitored electrical parameters is presented.
Software Visualization in the Large

Software is invisible, disappearing into les on disks. The invisible nature of software contributes to low programmer productivity by hiding system complexity, particularly for large team-oriented projects. Visualization can help software engineers cope with this complexity and thereby increase programmer productivity. We describe four innovative visual representations of software that scale to productionsized systems and illustrate their usage in ve software case studies involving: version history, di erencing, static properties, performance pro les, and dynamic program slices.
Individual Education Program ( IEP ) Paperwork : A Narrative Review

Previous studies have shown that teachers understand the importance of Individual Education Program (IEP), but they consider the administrative tasks of IEP as a burden. This review aims to illustrate how long the teacher completed the IEP administrative tasks, to explain why teachers view IEP as a burden, and to describe the strategies to minimize obstacles related to the administrative burden of IEP. The procedure of narrative review is selecting journals based on the inclusion and exclusion criteria related to administrative burden of IEP paperwork. The result shows that teachers spend more time doing IEP paperwork than assessing students’ assignments, communicating with parents, and sharing with colleagues. IEP paperwork takes up more than 10% of working time. The reasons IEP paperwork perceived as burdens are because of a large number of IEP forms and details, the multiple IEP service flow, the lack of knowledge of the personnel relating to the preparation or implementation of IEPs, the lack of assistance of administrative staff to complete the IEP paperwork, and the short/limited deadlines for administrative duties of IEP. The proposed strategies are improving appropriate technology, streamlining the contents of IEP forms, group IEP and increase the IEP administrative skills of the teachers.
A survey of B-tree locking techniques

B-trees have been ubiquitous in database management systems for several decades, and they are used in other storage systems as well. Their basic structure and basic operations are well and widely understood including search, insertion, and deletion. Concurrency control of operations in B-trees, however, is perceived as a difficult subject with many subtleties and special cases. The purpose of this survey is to clarify, simplify, and structure the topic of concurrency control in B-trees by dividing it into two subtopics and exploring each of them in depth.
Towards Enhancing the Security of OAuth Implementations in Smart Phones

With the roaring growth and wide adoption of smart mobile devices, users are continuously integrating with culture of the mobile applications (apps). These apps are not only gaining access to information on the smartphone but they are also able gain users' authorization to access remote servers on their behalf. The Open standard for Authorization (OAuth) is widely used in mobile apps for gaining access to user's resources on remote service providers. In this paper, we analyze the different OAuth implementations adopted by the SDKs of the popular resource providers on smartphones and demonstrate possible attacks on most OAuth implementations. By analyzing source code of more than 430 popular Android apps we summarized the trends followed by the service providers and by the OAuth development choices made by application developers. In addition, we propose an application-based OAuth Manager framework, that provides a secure OAuth flow in smartphones that is based on the concept of privilege separation and does not require high overhead.
A Relatively Small Turing Machine Whose Behavior Is Independent of Set Theory

Since the definition of the busy beaver function by Rado in 1962, an interesting open question has been the smallest value of n for which BB(n) is independent of Zermelo–Fraenkel set theory with the axiom of choice (ZFC). Is this n approximately 10, or closer to 1 000 000, or is it even larger? In this paper, we show that it is at most 7910 by presenting an explicit description of a 7910-state Turing machine Z with one tape and a two-symbol alphabet that cannot be proved to run forever in ZFC (even though it presumably does), assuming ZFC is consistent. The machine is based on work of Harvey Friedman on independent statements involving order-invariant graphs. In doing so, we give the first known upper bound on the highest provable busy beaver number in ZFC. To create Z, we develop and use a higher-level language, Laconic, which is much more convenient than direct state manipulation. We also use Laconic to design two Turing machines, G and R, that halt if and only if there are counterexamples to Goldbach’s conjecture and the Riemann hypothesis, respectively.
A generalized taxonomy of explanations styles for traditional and social recommender systems

Recommender systems usually provide explanations of their recommendations to better help users to choose products, activities or even friends. Up until now, the type of an explanation style was considered in accordance to the recommender system that employed it. This relation was one-to-one, meaning that for each different recommender systems category, there was a different explanation style category. However, this kind of one-to-one correspondence can be considered as over-simplistic and non generalizable. In contrast, we consider three fundamental resources that can be used in an explanation: users, items and features and any combination of them. In this survey, we define (i) the Human style of explanation, which provides explanations based on similar users, (ii) the Item style of explanation, which is based on choices made by a user on similar items and (iii) the Feature style of explanation, which explains the recommendation based on item features rated by the user beforehand. By using any combination of the aforementioned styles we can also define the Hybrid style of explanation. We demonstrate how these styles are put into practice, by presenting recommender systems that employ them. Moreover, since there is inadequate research in the impact of social web in contemporary recommender systems and their explanation styles, we study new emerged social recommender systems i.e. Facebook Connect explanations (HuffPo, Netflix, etc.) and geo-social explanations that combine geographical with social data (Gowalla, Facebook Places, etc.). Finally, we summarize the results of three different user studies, to support that Hybrid is the most effective explanation style, since it incorporates all other styles.
Estimation of Presentations Skills Based on Slides and Audio Features

This paper proposes a simple estimation of the quality of student oral presentations. It is based on the study and analysis of features extracted from the audio and digital slides of 448 presentations. The main goal of this work is to automatically predict the values assigned by professors to different criteria in a presentation evaluation rubric. Machine Learning methods were used to create several models that classify students in two clusters: high and low performers. The models created from slide features were accurate up to 65%. The most relevant features for the slide-base models were: number of words, images, and tables, and the maximum font size. The audio-based models reached up to 69% of accuracy, with pitch and filled pauses related features being the most significant. The relatively high degrees of accuracy obtained with these very simple features encourage the development of automatic estimation tools for improving presentation skills.
Sibyl: A Practical Internet Route Oracle

Network operators measure Internet routes to troubleshoot problems, and researchers measure routes to characterize the Internet. However, they still rely on decades-old tools like traceroute, BGP route collectors, and Looking Glasses, all of which permit only a single query about Internet routes—what is the path from here to there? This limited interface complicates answering queries about routes such as “find routes traversing the Level3/AT&T peering in Atlanta,” to understand the scope of a reported problem there. This paper presents Sibyl, a system that takes rich queries that researchers and operators express as regular expressions, then issues and returns traceroutes that match even if it has never measured a matching path in the past. Sibyl achieves this goal in three steps. First, to maximize its coverage of Internet routing, Sibyl integrates together diverse sets of traceroute vantage points that provide complementary views, measuring from thousands of networks in total. Second, because users may not know which measurements will traverse paths of interest, and because vantage point resource constraints keep Sibyl from tracing to all destinations from all sources, Sibyl uses historical measurements to predict which new ones are likely to match a query. Finally, based on these predictions, Sibyl optimizes across concurrent queries to decide which measurements to issue given resource constraints. We show that Sibyl provides researchers and operators with the routing information they need—in fact, it matches 76% of the queries that it could match if an oracle told it which measurements to issue.
Activity Forecasting

We address the task of inferring the future actions of people from noisy visual input. We denote this task activity forecasting. To achieve accurate activity forecasting, our approach models the effect of the physical environment on the choice of human actions. This is accomplished by the use of state-of-the-art semantic scene understanding combined with ideas from optimal control theory. Our unified model also integrates several other key elements of activity analysis, namely, destination forecasting, sequence smoothing and transfer learning. As proof-of-concept, we focus on the domain of trajectory-based activity analysis from visual input. Experimental results demonstrate that our model accurately predicts distributions over future actions of individuals. We show how the same techniques can improve the results of tracking algorithms by leveraging information about likely goals and trajectories.
Fast J-linkage algorithm for camera orientation applications

A computationally efficient approach to obtain vanishing points in images for camera orientation applications is presented in this paper. Modifications are made to the conventional J-linkage algorithm, named Fast J-linkage, so that the detection of vanishing points is achieved in a computationally efficient manner. This fast algorithm is then utilized to recover camera orientation or rotation matrices from video sequences. Both the computational efficiency and performance aspect of the Fast J-linkage algorithm are compared with the conventional J-linkage algorithm by examining the York Urban image database. In addition, the results of an actual camera rotation implementation is provided and validated by using inertial sensors data. The results obtained indicate the higher computational efficiency and accuracy aspects of the developed vanishing point detection.
Spatial Transformer Networks

Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner. In this work we introduce a new learnable module, the Spatial Transformer, which explicitly allows the spatial manipulation of data within the network. This differentiable module can be inserted into existing convolutional architectures, giving neural networks the ability to actively spatially transform feature maps, conditional on the feature map itself, without any extra training supervision or modification to the optimisation process. We show that the use of spatial transformers results in models which learn invariance to translation, scale, rotation and more generic warping, resulting in state-of-the-art performance on several benchmarks, and for a number of classes of transformations.