nils-herrmann/clean_scirepeval huggingface dataset:
Boring but important: a self-transcendent purpose for learning fosters academic self-regulation.

Many important learning tasks feel uninteresting and tedious to learners. This research proposed that promoting a prosocial, self-transcendent purpose could improve academic self-regulation on such tasks. This proposal was supported in 4 studies with over 2,000 adolescents and young adults. Study 1 documented a correlation between a self-transcendent purpose for learning and self-reported trait measures of academic self-regulation. Those with more of a purpose for learning also persisted longer on a boring task rather than giving in to a tempting alternative and, many months later, were less likely to drop out of college. Study 2 addressed causality. It showed that a brief, one-time psychological intervention promoting a self-transcendent purpose for learning could improve high school science and math grade point average (GPA) over several months. Studies 3 and 4 were short-term experiments that explored possible mechanisms. They showed that the self-transcendent purpose manipulation could increase deeper learning behavior on tedious test review materials (Study 3), and sustain self-regulation over the course of an increasingly boring task (Study 4). More self-oriented motives for learning--such as the desire to have an interesting or enjoyable career--did not, on their own, consistently produce these benefits (Studies 1 and 4).
GraphScript: implementing complex graph algorithms in SAP HANA

Real-world graph applications are typically domain-specific and model complex business processes in the property graph data model. To implement a domain-specific graph algorithm in the context of such a graph application, simply providing a set of built-in graph algorithms is usually not sufficient nor does it allow algorithm customization to the user's needs. To cope with these issues, graph database vendors provide---in addition to their declarative graph query languages---procedural interfaces to write user-defined graph algorithms.
 In this paper, we introduce GraphScript, a domain-specific graph query language tailored to serve advanced graph analysis tasks and the specification of complex graph algorithms. We describe the major language design of GraphScript, discuss graph-specific optimizations, and describe the integration into an enterprise data platform.
Bidirectional A* Search with Additive Approximation Bounds

In this paper, we present new theoretical and experimental results for bidirectional A∗ search. Unlike most previous research on this topic, our results do not require assumptions of either consistent or balanced heuristic functions for the search. Our theoretical work examines new results on the worst-case number of node expansions for inconsistent heuristic functions with bounded estimation errors. Additionally, we consider several alternative termination criteria in order to more quickly terminate the bidirectional search, and we provide worst-case approximation bounds for our suggested criteria. We prove that our approximation bounds are purely additive in nature (a general improvement over previous multiplicative approximations). Experimental evidence on large-scale road networks suggests that the errors introduced are truly quite negligible in practice, while the performance gains are significant.
MultiUN v2: UN Documents with Multilingual Alignments

MultiUN is a multilingual parallel corpus extracted from th e official documents of the United Nations. It is available in the six official languages of the UN and a small portion of it is also available in German. This paper presents a major update on the first publ ic version of the corpus released in 2010. This version 2 consists of ove r 513, 091 documents, including around 9% of new documents retrieved from the United Nations official document system. Compared t o the first release, we applied several modifications to the co rpus preparation method. In this paper, we describe the methods w e used for processing the UN documents and aligning the sente nces. The most significant improvement compared to the previous relea s is the newly added multilingual sentence alignment infor mation. The alignment information is encoded together with the text in X ML instead of additional files. Our representation of the sen tence alignment allows quick construction of aligned texts parallel in arbi trary number of languages, which is essential for building m achine translation systems.
A literature review on the prosthetic treatment of structurally compromised teeth.

This article presents a review of the literature on biomechanical factors affecting the treatment outcome of prosthetic treatment of structurally compromised dentitions, with the main emphasis on often-compromised endodontically treated teeth.



Articles cited in a MEDLINE/PubMed search were reviewed with a focus on factors influencing the risk for fatigue failures.



Technical failures in connection with fixed prosthodontics are often caused by fatigue fractures. The abutments, cement, and reconstruction are all subjected to stress caused by occlusal forces, and fatigue fracture may occur at the weakest point or where the maximum stress occurs. The weakest point is frequently in connection with endodontically treated teeth restored with posts and cores.



The literature points to nonaxial forces as a risk for fatigue fracture of teeth, cement, and restorative material. Favorable occlusal prosthesis design is probably more important for survival of structurally compromised endodontically treated teeth than is the type of post used.
Integrating Web 2.0 Technologies into Face-to-Face PBL to Support Producing, Storing, and Sharing Content in a Higher Education Course.

In this article we present and discuss the process of developing and implementing a PBL-based course entitled Moving Images in Teaching and Learning that was held at the University of Lapland, Finland. In the course of the project, this fairly traditional face-to-face course was redesigned into a blended PBL course by integrating Web 2.0 applications into the course. The pedagogical rationale was to support students’ meaningful learning in various phases of the PBL process, and to enable easy storage and sharing of the ideas produced by the students. In addition, the rationale was to create more opportunities for students to report on their process during their independent knowledge acquisition phase and for the teacher to monitor and support students’ information searching processes. We focus mainly on students’ experiences of the implementation and conclude with ideas about our next steps.
The inquisitorial trial of a cross-dressing lesbian: reactions and responses to female homosexuality in 18th-century portugal.

This article analyzes the inquisitorial trial of Maria Duran, a Catalan novice in the Dominican convent of Nossa Senhora do Paraíso in Portugal. Maria Duran was arrested by the Inquisition in 1741 and, after a lengthy trial, condemned in 1744 to a public lashing and exile. She was suspected of having made a pact with the Devil and was accused by many female witnesses of possessing a "secret penis" that she had allegedly used in her amorous relations with fellow nuns and novices. Her voluminous trial dossier offers a rare and fascinating documentary insight into the often extreme reactions that female homosexuality provoked from both men and women in early modern Portugal. Using the evidence offered by the 18th-century trial of Maria Duran, this article highlights female bewilderment when faced with female-on-female sexual violence and the difficulty that men (in this case, churchmen) had coming to terms with the existence of female homosexuality. It also discusses the case in light of the acts/identity debate among historians of the history of sexuality.
Forehead augmentation with alloplastic implants.

Methods of alloplastic forehead augmentation using soft expanded polytetrafluoroethylene (ePTFE) and silicone implants are described. Soft ePTFE forehead implantation has the advantage of being technically simpler, with better fixation. The disadvantages are a limited degree of forehead augmentation and higher chance of infection. Properly fabricated soft silicone implants provide potential for larger degree of forehead silhouette augmentation with less risk of infection. The corrugated edge and central perforations of the implant minimize mobility and capsule contraction.
An obstacle signaling system for the blind

Many commercial navigation aids suffer from a number of design flaws, the most important of which are related to the human interface that conveys information to the user. Aids for the visually impaired are lightweight electronic devices that are either incorporated into a long cane, hand-held or worn by the client, warning of hazards ahead. Most aids use vibrating buttons or sound alerts to warn of upcoming obstacles, a method which is only capable of conveying very crude information regarding direction and proximity to the nearest object. Some of the more sophisticated devices use a complex audio interface in order to deliver more detailed information, but this often compromises the user's hearing, a critical impairment for a blind user. The author has produced an original design and working prototype solution which is a major first step in addressing some of these faults found in current production models for the blind.
Parallelization Strategies for Ant Colony Optimization

Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied toNP-hard combinatorial optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, that of executing parallel independent runs of an algorithm. The empirical tests are performed applyingMAX–MIN Ant System, one of the most efficient ACO algorithms, to the Traveling Salesman Problem and show that using parallel independent runs is very effective.
Autocorrelation and DCT based quality metrics for fingerprint samples generated by smartphones

It is becoming feasible and promising to use general purposed smartphone cameras as fingerprint scanners due to the rapidly improvement of smartphone hardware performance. We propose an approach to qualify the fingerprint samples generated by smartphones' cameras under real-life scenarios. Firstly, our approach extracts 6 quality features for each image block divided from a fingerprint sample using ridge patterns' spatial autocorrelation in both the spatial and the discrete cosine transform (DCT) domain. Secondly, a trained support vector machine is adopted to generate a binary decision to indicate the quality of the image block. Finally, we take the normalized count of qualified blocks as an indicator of the whole fingerprint sample's quality. Our experiments demonstrate that the proposed approach is effective to assess the quality of fingerprint samples captured by such general purposed smartphone cameras. A Spearman's rank correlation coefficient (ranging between [-1,1]) of 0.6354 is achieved between the proposed quality metric and samples' normalized comparison scores (as a ground truth) in our experiment.
Utilizing correlated node mobility for efficient DTN routing

In a delay tolerant network (DTN), nodes are connected intermittently and the future node connections are mostly not known. Therefore, effective forwarding based on limited knowledge of contact behavior of nodes is challenging. Most of the previous studies assumed that mobility of a node is independent from mobility of other nodes and looked at only the pairwise node relations to decide routing. In contrast, in this paper, we analyze the temporal correlation between the meetings of each node with other nodes and utilize this correlation for efficient routing. We introduce a new metric called conditional intermeeting time (CIT), which computes the average intermeeting time between two nodes relative to a meeting with a third node. Then, we modify existing DTN routing protocols using the proposed metric to improve their performance. Extensive simulations based on real and synthetic DTN traces show that the modified algorithms perform better than the original ones.
A comparison between extended kalman filtering and sequential monte carlo technique for simultaneous localisation and map-building.

Monte Carlo Localisation has been applied to solve many different classes of localisation problems. In this paper, we present a possible Simultaneous Localisation and Map-building implementation using the Sequential Monte Carlo technique. Multiple particle filters are created to estimate both the robot and landmark positions simultaneously. The proposed technique shows promising results when compared with those obtained with the Extended Kalman filter.
Mid-Price Prediction in a Limit Order Book

We propose several nonparametric predictors of the mid-price in a limit order book, based on different features constructed from the order book data observed contemporaneously. contemporaneously and in the recent past. We evaluate our predictors in the context of an order execution task by constructing order execution strategies that incorporate these predictors. In our evaluations, we use a large dataset of historical order placements, cancellations, and trades over a five-month period from 2013 to 2014 for liquid stocks traded on NASDAQ. We show that some of the features achieve statistically significant improvements compared to some standard strategies that do not incorporate price forecasting. For the two features that achieve the best performance, the trading cost improvement is on the order of one basis point, which can be economically very significant for asset managers with large portfolio turnovers and for brokers with considerable trading volumes.
DDCO: Discovery of Deep Continuous Options forRobot Learning from Demonstrations

An option is a short-term skill consisting of a control policy for a specified region of the state space, and a termination condition recognizing leaving that region. In prior work, we proposed an algorithm called Deep Discovery of Options (DDO) to discover options to accelerate reinforcement learning in Atari games. This paper studies an extension to robot imitation learning, called Discovery of Deep Continuous Options (DDCO), where low-level continuous control skills parametrized by deep neural networks are learned from demonstrations. We extend DDO with: (1) a hybrid categorical–continuous distribution model to parametrize high-level policies that can invoke discrete options as well continuous control actions, and (2) a cross-validation method that relaxes DDO’s requirement that users specify the number of options to be discovered. We evaluate DDCO in simulation of a 3-link robot in the vertical plane pushing a block with friction and gravity, and in two physical experiments on the da Vinci surgical robot, needle insertion where a needle is grasped and inserted into a silicone tissue phantom, and needle bin picking where needles and pins are grasped from a pile and categorized into bins. In the 3-link arm simulation, results suggest that DDCO can take 3x fewer demonstrations to achieve the same reward compared to a baseline imitation learning approach. In the needle insertion task, DDCO was successful 8/10 times compared to the next most accurate imitation learning baseline 6/10. In the surgical bin picking task, the learned policy successfully grasps a single object in 66 out of 99 attempted grasps, and in all but one case successfully recovered from failed grasps by retrying a second time.
Mono-camera based simultaneous obstacle recognition and distance estimation for obstacle avoidance of power transmission lines inspection robot

This paper presents a mono-camera based simultaneous obstacle recognition and distance estimation method for power transmission lines(PTLs) inspection robot to avoid obstacles on or around them. The proposed robot inspects the PTLs while moving along them between power transmission towers. For autonomous navigation, our robot recognizes obstacles and avoids a collision with them. In addition, it can stop at the ends of the PTLs by recognizing its structures, such as insulators, installed at the ends of them. In order to recognize obstacles or insulators efficiently, initially, a robust PTLs detection method based on vanishing point estimation is proposed since they are installed on or around the PTLs. Then, obstacle models with various scales are built, and multiple regions-of-interest(ROIs) according to the scales of the model are constructed along the detected PTLs. Finally, obstacles are recognized within the multiple ROIs. Because each ROI represents a corresponding scale of the obstacle model to be matched, the proposed approach can efficiently deal with scale changes and also estimate distance between the robot and the obstacle simultaneously with obstacle recognition. Experimental results not only show that the proposed method correctly recognizes obstacles but also that the distance between the robot and the obstacles is estimated efficiently.
Waveguide-Stripline Series–Corporate Hybrid Feed Technique for Dual-Polarized Antenna Array Applications

This paper proposes a waveguide-stripline series–corporate hybrid feed technique to ease the feed-network design for dual-polarized antenna arrays. The hybrid feed network consists of a stripline series feed network and a waveguide-stripline corporate feed network, incorporating both of their advantages in one application. Furthermore, the design can efficiently simplify the manufacturing and packaging process without any bonding postprocess. The proposed technique is realized on an  $8 \times 8$  dual-polarized antenna array, and the expected performances are verified by measurement. Experimental results show that the antenna array can operate in a wide bandwidth of 13.2% with more than 40 dB isolation for dual polarization. Cross polarizations at the center frequency of 15.2 GHz are below −27 dB, and the achievable aperture efficiency is 80% for both the polarizations. This hybrid feed approach can be considered as a promising solution for efficient feed-network applications.
Safe Reinforcement Learning via Formal Methods: Toward Safe Control Through Proof and Learning

Formal verification provides a high degree of confidence in safe system operation, but only if reality matches the verified model. Although a good model will be accurate most of the time, even the best models are incomplete. This is especially true in Cyber-Physical Systems because high-fidelity physical models of systems are expensive to develop and often intractable to verify. Conversely, reinforcement learning-based controllers are lauded for their flexibility in unmodeled environments, but do not provide guarantees of safe operation. This paper presents an approach for provably safe learning that provides the best of both worlds: the exploration and optimization capabilities of learning along with the safety guarantees of formal verification. Our main insight is that formal verification combined with verified runtime monitoring can ensure the safety of a learning agent. Verification results are preserved whenever learning agents limit exploration within the confounds of verified control choices as long as observed reality comports with the model used for off-line verification. When a model violation is detected, the agent abandons efficiency and instead attempts to learn a control strategy that guides the agent to a modeled portion of the state space. We prove that our approach toward incorporating knowledge about safe control into learning systems preserves safety guarantees, and demonstrate that we retain the empirical performance benefits provided by reinforcement learning. We also explore various points in the design space for these justified speculative controllers in a simple model of adaptive cruise control model for autonomous cars.
Process Mining Software Repositories

Software developers’ activities are in general recorded in software repositories such as version control systems, bug trackers and mail archives. While abundant information is usually present in such repositories, successful information extraction is often challenged by the necessity to simultaneously analyze different repositories and to combine the information obtained. We propose to apply process mining techniques, originally developed for business process analysis, to address this challenge. However, in order for process mining to become applicable, different software repositories should be combined, and “related” software development events should be matched: e.g., mails sent about a file, modifications of the file and bug reports that can be traced back to it. The combination and matching of events has been implemented in FRASR (Framework for Analyzing Software Repositories), augmenting the process mining framework ProM. FRASR has been successfully applied in a series of case studies addressing such aspects of the development process as roles of different developers and the way bug reports are handled.
Analysis of Intel's Haswell Microarchitecture Using The ECM Model and Microbenchmarks

This paper presents an in-depth analysis of Intel’s Haswell microarchitecture for streaming loop kernels. Among the new features examined is the dual-ring Uncore design, Cluster-on-Die mode, Uncore Frequency Scaling, core improvements as new and improved execution units, as well as improvements throughout the memory hierarchy. The Execution-Cache-Memory diagnostic performance model is used together with a generic set of microbenchmarks to quantify the efficiency of the microarchitecture. The set of microbenchmarks is chosen such that it can serve as a blueprint for other streaming loop kernels.
Software-Defined Multi-cloud Computing: A Vision, Architectural Elements, and Future Directions

Cloud computing has been emerged in the last decade to enable utility-based computing resource management without purchasing hardware equipment. Cloud providers run multiple data centers in various locations to manage and provision the Cloud resources to their customers. More recently, the introduction of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) opens more opportunities in Clouds which enables dynamic and autonomic configuration and provisioning of the resources in Cloud data centers. This paper proposes architectural framework and principles for Programmable Network Clouds hosting SDNs and NFVs for geographically distributed MultiCloud computing environments. Cost and SLA-aware resource provisioning and scheduling that minimizes the operating cost without violating the negotiated SLAs are investigated and discussed in regards of techniques for autonomic and timely VNF composition, deployment and management across multiple Clouds. We also discuss open challenges and directions for creating auto-scaling solutions for performance optimization of VNFs using analytics and monitoring techniques, algorithms for SDN controller for scalable traffic and deployment management. The simulation platform and the proof-of-concept prototype are presented with initial evaluation results.
Sentiment analysis: capturing favorability using natural language processing

This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.