nils-herrmann/clean_scirepeval huggingface dataset:
Reliable State Monitoring in Cloud Datacenters

State monitoring is widely used for detecting critical events and abnormalities of distributed systems. As the scale of such systems grows and the degree of workload consolidation increases in Cloud data centers, node failures and performance interferences, especially transient ones, become the norm rather than the exception. Hence, distributed state monitoring tasks are often exposed to impaired communication caused by such dynamics on different nodes. Unfortunately, existing distributed state monitoring approaches are often designed under the assumption of always-online distributed monitoring nodes and reliable inter-node communication. As a result, these approaches often produce misleading results which in turn introduce various problems to Cloud users who rely on state monitoring results to perform automatic management tasks such as auto-scaling. This paper introduces a new state monitoring approach that tackles this challenge by exposing and handling communication dynamics such as message delay and loss in Cloud monitoring environments. Our approach delivers two distinct features. First, it quantitatively estimates the accuracy of monitoring results to capture uncertainties introduced by messaging dynamics. This feature helps users to distinguish trustworthy monitoring results from ones heavily deviated from the truth, yet significantly improves monitoring utility compared with simple techniques that invalidate all monitoring results generated with the presence of messaging dynamics. Second, our approach also adapts to non-transient messaging issues by reconfiguring distributed monitoring algorithms to minimize monitoring errors. Our experimental results show that, even under severe message loss and delay, our approach consistently improves monitoring accuracy, and when applied to Cloud application auto-scaling, outperforms existing state monitoring techniques in terms of the ability to correctly trigger dynamic provisioning.
Need for speed: CORA scheduler for optimizing completion-times in the cloud

There is an increasing need for cloud service performance that can be tailored to customer requirements. In the context of jobs submitted to cloud computing clusters, a crucial requirement is the specification of job completion-times. A natural way to model this specification, is through client/job utility functions that are dependent on job completion-times. We present a method to allocate and schedule heterogeneous resources to jointly optimize the utilities of jobs in a cloud. Specifically: (i) we formulate a completion-time optimal resource allocation (CORA) problem to apportion cluster resources across the jobs that enforces max-min fairness among job utilities, and (ii) starting with an integer programming problem, we perform a series of steps to transform it into an equivalent linear programming problem, and (iii) we implement the proposed framework as a utility-aware resource scheduler in the widely used Hadoop data processing framework, and finally (iv) through extensive experiments with real-world datasets, we show that our prototype achieves significant performance improvement over existing resource-allocation policies.
Higher-Order Abstract Syntax

We describe motivation, design, use, and implementation of higher-order abstract syntax as a central representation for programs, formulas, rules, and other syntactic objects in program manipulation and other formal systems where matching and substitution or unification are central operations. Higher-order abstract syntax incorporates name binding information in a uniform and language generic way. Thus it acts as a powerful link integrating diverse tools in such formal environments. We have implemented higher-order abstract syntax, a supporting matching and unification algorithm, and some clients in Common Lisp in the framework of the Ergo project at Carnegie Mellon University.
A practical scalable distributed B-tree

Internet applications increasingly rely on scalable data structures that must support high throughput and store huge amounts of data. These data structures can be hard to implement efficiently. Recent proposals have overcome this problem by giving up on generality and implementing specialized interfaces and functionality (e.g., Dynamo ). We present the design of a more general and flexible solution: a fault-tolerant and scalable distributed B-tree. In addition to the usual B-tree operations, our B-tree provides some important practical features: transactions for atomically executing several operations in one or more B-trees, online migration of B-tree nodes between servers for load-balancing, and dynamic addition and removal of servers for supporting incremental growth of the system. Our design is conceptually simple. Rather than using complex concurrency and locking protocols, we use distributed transactions to make changes to B-tree nodes. We show how to extend the B-tree and keep additional information so that these transactions execute quickly and efficiently. Our design relies on an underlying distributed data sharing service, Sinfonia , which provides fault tolerance and a light-weight distributed atomic primitive. We use this primitive to commit our transactions. We implemented our B-tree and show that it performs comparably to an existing open-source B-tree and that it scales to hundreds of machines. We believe that our approach is general and can be used to implement other distributed data structures easily.
Network Dissection: Quantifying Interpretability of Deep Visual Representations

We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts. Given any CNN model, the proposed method draws on a data set of concepts to score the semantics of hidden units at each intermediate convolutional layer. The units with semantics are labeled across a broad range of visual concepts including objects, parts, scenes, textures, materials, and colors. We use the proposed method to test the hypothesis that interpretability is an axis-independent property of the representation space, then we apply the method to compare the latent representations of various networks when trained to solve different classification problems. We further analyze the effect of training iterations, compare networks trained with different initializations, and measure the effect of dropout and batch normalization on the interpretability of deep visual representations. We demonstrate that the proposed method can shed light on characteristics of CNN models and training methods that go beyond measurements of their discriminative power.
Real-time automatic license plate recognition for CCTV forensic applications

We propose an efficient real-time automatic license plate recognition (ALPR) framework, particularly designed to work on CCTV video footage obtained from cameras that are not dedicated to the use in ALPR. At present, in license plate detection, tracking and recognition are reasonably well-tackled problems with many successful commercial solutions being available. However, the existing ALPR algorithms are based on the assumption that the input video will be obtained via a dedicated, high-resolution, high-speed camera and is/or supported by a controlled capture environment, with appropriate camera height, focus, exposure/shutter speed and lighting settings. However, typical video forensic applications may require searching for a vehicle having a particular number plate on noisy CCTV video footage obtained via non-dedicated, medium-to-low resolution cameras, working under poor illumination conditions. ALPR in such video content faces severe challenges in license plate localization, tracking and recognition stages. This paper proposes a novel approach for efficient localization of license plates in video sequence and the use of a revised version of an existing technique for tracking and recognition. A special feature of the proposed approach is that it is intelligent enough to automatically adjust for varying camera distances and diverse lighting conditions, a requirement for a video forensic tool that may operate on videos obtained by a diverse set of unspecified, distributed CCTV cameras.
Towards lifelong feature-based mapping in semi-static environments

The feature-based graphical approach to robotic mapping provides a representationally rich and computationally efficient framework for an autonomous agent to learn a model of its environment. However, this formulation does not naturally support long-term autonomy because it lacks a notion of environmental change; in reality, “everything changes and nothing stands still, ” and any mapping and localization system that aims to support truly persistent autonomy must be similarly adaptive. To that end, in this paper we propose a novel feature-based model of environmental evolution over time. Our approach is based upon the development of an expressive probabilistic generative feature persistence model that describes the survival of abstract semi-static environmental features over time. We show that this model admits a recursive Bayesian estimator, the persistence filter, that provides an exact online method for computing, at each moment in time, an explicit Bayesian belief over the persistence of each feature in the environment. By incorporating this feature persistence estimation into current state-of-the-art graphical mapping techniques, we obtain a flexible, computationally efficient, and information-theoretically rigorous framework for lifelong environmental modeling in an ever-changing world.
Social Interactions in Massively Multiplayer Online Role-Playing Gamers

To date, most research into massively multiplayer online role-playing games (MMORPGs) has examined the demographics of play. This study explored the social interactions that occur both within and outside of MMORPGs. The sample consisted of 912 self-selected MMORPG players from 45 countries. MMORPGs were found to be highly socially interactive environments providing the opportunity to create strong friendships and emotional relationships. The study demonstrated that the social interactions in online gaming form a considerable element in the enjoyment of playing. The study showed MMORPGs can be extremely social games, with high percentages of gamers making life-long friends and partners. It was concluded that virtual gaming may allow players to express themselves in ways they may not feel comfortable doing in real life because of their appearance, gender, sexuality, and/or age. MMORPGs also offer a place where teamwork, encouragement, and fun can be experienced.
Focused Crawling for Structured Data

The Web is rapidly transforming from a pure document collection to the largest connected public data space. Semantic annotations of web pages make it notably easier to extract and reuse data and are increasingly used by both search engines and social media sites to provide better search experiences through rich snippets, faceted search, task completion, etc. In our work, we study the novel problem of crawling structured data embedded inside HTML pages. We describe Anthelion, the first focused crawler addressing this task. We propose new methods of focused crawling specifically designed for collecting data-rich pages with greater efficiency. In particular, we propose a novel combination of online learning and bandit-based explore/exploit approaches to predict data-rich web pages based on the context of the page as well as using feedback from the extraction of metadata from previously seen pages. We show that these techniques significantly outperform state-of-the-art approaches for focused crawling, measured as the ratio of relevant pages and non-relevant pages collected within a given budget.
A Combination Approach to Web User Profiling

In this article, we study the problem of Web user profiling, which is aimed at finding, extracting, and fusing the “semantic”-based user profile from the Web. Previously, Web user profiling was often undertaken by creating a list of keywords for the user, which is (sometimes even highly) insufficient for main applications. This article formalizes the profiling problem as several subtasks: profile extraction, profile integration, and user interest discovery. We propose a combination approach to deal with the profiling tasks. Specifically, we employ a classification model to identify relevant documents for a user from the Web and propose a Tree-Structured Conditional Random Fields (TCRF) to extract the profile information from the identified documents; we propose a unified probabilistic model to deal with the name ambiguity problem (several users with the same name) when integrating the profile information extracted from different sources; finally, we use a probabilistic topic model to model the extracted user profiles, and construct the user interest model. Experimental results on an online system show that the combination approach to different profiling tasks clearly outperforms several baseline methods. The extracted profiles have been applied to expert finding, an important application on the Web. Experiments show that the accuracy of expert finding can be improved (ranging from +6% to +26% in terms of MAP) by taking advantage of the profiles.
SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

The final goal of Interpretable Semantic Textual Similarity (iSTS) is to build systems that explain which are the differences and commonalities between two sentences. The task adds an explanatory level on top of STS, formalized as an alignment between the chunks in the two input sentences, indicating the relation and similarity score of each alignment. The task provides train and test data on three datasets: news headlines, image captions and student answers. It attracted nine teams, totaling 20 runs. All datasets and the annotation guideline are freely available1
Characteristics of external genitalia in pre- and postmenopausal women.

To determine those objective measurements that characterize the differences between the external genital organs of pre- and postmenopausal women.



During the study period, 50 premenopausal and 50 postmenopausal patients were recruited. Only women who were admitted for routine control examinations were consecutively included in the study. Exclusion criteria were previous history of pelvic surgery including external and internal genital organs, presence of diseases that may change the anatomy of external genital organs, Mullerian anomalies, previous vaginal birth with mediolateral episiotomy, and use of hormone replacement therapy. The following measurements were performed: length and width of clitoris, labium majus, and labium minus, the distance between the clitoris and urethra, perineal length, and length of vagina.



The length of the vagina and the width of the labium minus were significantly different between the two groups. Mean vaginal length was significantly longer in premenopausal women compared to postmenopausal women (90.3 +/- 14.8 mm vs. 82.3 +/- 11.2 mm, respectively). The labia minora were wider in premenopausal women than in postmenopausal women (17.9 +/- 4.1 mm vs. 15.4 +/- 4.7 mm).



Characterization of the anatomical changes and relationships of external genitalia in postmenopausal women is important for functional and perioperative evaluation. In addition to reconstructive surgical procedures, determination of the objective measurements of anatomical landmarks in postmenopausal external genitalia might also be useful for assessing the results of treatment of 'atrophic' changes in women.
Simultaneous linear estimation of multiple view geometry and lens distortion

A bugbear of uncalibrated stereo reconstruction is that cameras which deviate from the pinhole model have to be pre-calibrated in order to correct for nonlinear lens distortion. If they are not, and point correspondence is attempted using the uncorrected images, the matching constraints provided by the fundamental matrix must be set so loose that point matching is significantly hampered. This paper shows how linear estimation of the fundamental matrix from two-view point correspondences may be augmented to include one term of radial lens distortion. This is achieved by (1) changing from the standard radiallens model to another which (as we show) has equivalent power, but which takes a simpler form in homogeneous coordinates, and (2) expressing fundamental matrix estimation as a Quadratic Eigenvalue Problem (QEP), for which efficient algorithms are well known. I derive the new estimator, and compare its performance against bundle-adjusted calibration-grid data. The new estimator is fast enough to be included in a RANSAC-based matching loop, and we show cases of matching being rendered possible by its use. I show how the same lens can be calibrated in a natural scene where the lack of straight lines precludes most previous techniques. The modification when the multi-view relation is a planar homography or trifocal tensor is described.
The Benefits of Interactions with Physically Present Robots over Video-Displayed Agents

This paper explores how a robot’s physical presence affects human judgments of the robot as a social partner. For this experiment, participants collaborated on simple book-moving tasks with a humanoid robot that was either physically present or displayed via a live video feed. Multiple tasks individually examined the following aspects of social interaction: greetings, cooperation, trust, and personal space. Participants readily greeted and cooperated with the robot whether present physically or in live video display. However, participants were more likely both to fulfill an unusual request and to afford greater personal space to the robot when it was physically present, than when it was shown on live video. The same was true when the live video displayed robot’s gestures were augmented with disambiguating 3-D information. Questionnaire data support these behavioral findings and also show that participants had an overall more positive interaction with the physically present
A Novel Structure for Single-Switch Nonisolated Transformerless Buck–Boost DC–DC Converter

A novel transformerless buck-boost dc-dc converter is proposed in this paper. The presented converter voltage gain is higher than that of the conventional boost, buck-boost, CUK, SEPIC, and ZETA converters, and high voltage can be obtained with a suitable duty cycle. In this converter, only one power switch is utilized. The voltage stress across the power switch is low. Hence, the low on-state resistance of the power switch can be selected to decrease conduction loss of the switch and improve efficiency. The presented converter has simple structure, therefore, the control of the proposed converter will be easy. The principle of operation and the mathematical analyses of the proposed converter are explained. The validity of the presented converter is verified by the experimental results.
Hidden Two-Stream Convolutional Networks for Action Recognition

Analyzing videos of human actions involves understanding the temporal relationships among video frames. State-of-the-art action recognition approaches rely on traditional optical flow estimation methods to pre-compute motion information for CNNs. Such a two-stage approach is computationally expensive, storage demanding, and not endto-end trainable. In this paper, we present a novel CNN architecture that implicitly captures motion information between adjacent frames. We name our approach hidden two-stream CNNs because it only takes raw video frames as input and directly predicts action classes without explicitly computing optical flow. Our end-to-end approach is 10x faster than its two-stage baseline. Experimental results on four challenging action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show that our approach significantly outperforms the previous best real-time approaches.
Virtual Model Control: An Intuitive Approach for Bipedal Locomotion

Virtual model control is a motion control framework that uses virtual components to create virtual forces generated when the virtual components interact with a robot system. An algorithm derived based on the virtual model control framework is applied to a physical planar bipedal robot. It uses a simple set of virtual components that allows the robot to walk successfully over level terrain. This paper also describes how the algorithm can be augmented for rough terrain walking based on geometric consideration. The resulting algorithm is very simple and does not require the biped to have an extensive sensory system. The robot does not know the slope gradients and transition locations in advance. The ground is detected using foot contact switches. Using the algorithm, we have successfully compelled a simulated seven-link planar biped to walk blindly up and down slopes and over rolling terrain. KEY WORDS—biped, legged locomotion, virtual model control, impedance control
The impact of online video lecture recordings and automated feedback on student performance

To what extent a blended learning configuration of face-to-face lectures, online on-demand video recordings of the face-to-face lectures and the offering of online quizzes with appropriate feedback has an additional positive impact on the performance of these students compared to the traditional face-toface course approach? In a between-subjects design in which students were randomly assigned to a group having access to the online lectures including multiple choice quizzes and appropriate feedback or to a group having access to the online lectures only, 474 students (161 men and 313 women) of a course on European Law agreed to participate in the experiment. By using regression analysis we found that the course grade of the students was predicted by their grade point average, their study discipline, their grade goal for the course, the expected difficulty-level of the course, the number of online lectures they viewed, the number of lectures the students attended in person and the interaction between the lectures they viewed online and attended in person. Students who attended few lectures had more benefit from viewing online lectures than students who attended many lectures. In contrast to our expectations, the regression analysis did not show a significant effect of automated feedback on student performance. Offering recordings of face-to-face lectures is an easy extension of a traditional course and is of practical importance, because it enables students who are often absent from the regular face-to-face lectures to be able to improve their course grade by viewing the lectures online.
Printing teddy bears: a technique for 3D printing of soft interactive objects

This paper considers the design, construction, and example use of a new type of 3D printer which fabricates three-dimensional objects from soft fibers (wool and wool blend yarn). This printer allows the substantial advantages of additive manufacturing techniques (including rapid turn-around prototyping of physical objects and support for high levels of customization and configuration) to be employed with a new class of material. This material is a form of loose felt formed when fibers from an incoming feed of yarn are entangled with the fibers in layers below it. The resulting objects recreate the geometric forms specified in the solid models which specify them, but are soft and flexible -- somewhat reminiscent in character to hand knitted materials. This extends 3D printing from typically hard and precise forms into a new set of forms which embody a different aesthetic of soft and imprecise objects, and provides a new capability for researchers to explore the use of this class of materials in interactive devices.