nils-herrmann/clean_scirepeval huggingface dataset:
Active-Constructive-Interactive: A Conceptual Framework for Differentiating Learning Activities

Active, constructive, and interactive are terms that are commonly used in the cognitive and learning sciences. They describe activities that can be undertaken by learners. However, the literature is actually not explicit about how these terms can be defined; whether they are distinct; and whether they refer to overt manifestations, learning processes, or learning outcomes. Thus, a framework is provided here that offers a way to differentiate active, constructive, and interactive in terms of observable overt activities and underlying learning processes. The framework generates a testable hypothesis for learning: that interactive activities are most likely to be better than constructive activities, which in turn might be better than active activities, which are better than being passive. Studies from the literature are cited to provide evidence in support of this hypothesis. Moreover, postulating underlying learning processes allows us to interpret evidence in the literature more accurately. Specifying distinct overt activities for active, constructive, and interactive also offers suggestions for how learning activities can be coded and how each kind of activity might be elicited.
Extracting Opinion Expressions with semi-Markov Conditional Random Fields

Extracting opinion expressions from text is usually formulated as a token-level sequence labeling task tackled using Conditional Random Fields (CRFs). CRFs, however, do not readily model potentially useful segment-level information like syntactic constituent structure. Thus, we propose a semi-CRF-based approach to the task that can perform sequence labeling at the segment level. We extend the original semi-CRF model (Sarawagi and Cohen, 2004) to allow the modeling of arbitrarily long expressions while accounting for their likely syntactic structure when modeling segment boundaries. We evaluate performance on two opinion extraction tasks, and, in contrast to previous sequence labeling approaches to the task, explore the usefulness of segmentlevel syntactic parse features. Experimental results demonstrate that our approach outperforms state-of-the-art methods for both opinion expression tasks.
Polygon-Invariant Generalized Hough Transform for High-Speed Vision-Based Positioning

The generalized Hough transform (GHT) is widely used for detecting or locating objects under similarity transformation. However, a weakness of the traditional GHT is its large storage requirement and time-consuming computational complexity due to the 4-D parameter space voting strategy. In this paper, a polygon-invariant GHT (PI-GHT) algorithm, as a novel scale- and rotation-invariant template matching method, is presented for high-speed object vision-based positioning. To demonstrate the performance of PI-GHT, several experiments were carried out to compare this novel algorithm with the other five popular matching methods. Experimental results show that the computational effort required by PI-GHT is smaller than that of the common methods due to the similarity transformations applied to the scale- and rotation-invariant triangle features. Moreover, the proposed PI-GHT maintains inherent robustness against partial occlusion, noise, and nonlinear illumination changes, because the local triangle features are based on the gradient directions of edge points. Consequently, PI-GHT is implemented in packaging equipment for radio frequency identification devices at an average time of 4.13 ms and 97.06% matching rate, to solder paste printing at average time nearly 5 ms with 99.87%. PI-GHT is applied to LED manufacturing equipment to locate multiobjects at least five times improvement in speed with a 96% matching rate.
Question generation for learning evaluation

In the last decade the electronic learning became a very useful tool in the studentspsilas education from different activity domains. The accomplished studies indicated that the students substantially appreciate the e-learning method, due to the facilities: the facile information access, a better storage of the didactic material, the curricula harmonization between universities, personalized instruction. The paper presents a software tool that can be used in the e-learning process in order to automatically generate questions from course materials, based on a series of tags defined by the professor. The test creator tool permits generation of questions based on electronic materials that students have. The solution implies teachers to have a series of tags and templates that they manage. These tags are used to generate question automatically.
Towards structured log analysis

Value of software log file analysis has been constantly increasing with the value of information to organizations. Log management tools still have a lot to deliver in order to empower their customers with the true strength of log information. In addition to the traditional uses such as testing software functional conformance, troubleshooting and performance benchmarking, log analysis has proven its capabilities in fields like intrusion detection and compliance evaluation. This is verified by the emphasis on log analysis in regulations like PCI DSS, FISMA, HIPAA and frameworks such as ISO 27001 and COBIT. In this paper we present an in depth analysis into current log analysis domains and common problems. A practical guide to the use of few popular log analysis tools is also included. Lack of proper support for structured analysis is identified as one major flaw in existing tools. After that, we describe a framework we developed for structured log analysis with the view of providing a solution to open problems in the domain. The core strength of the framework is its ability to handle many log file formats that are not well served by existing tools and providing sophisticated infrastructure for automating recurring log analysis procedures. We prove the usefulness of the framework with a simple experiment.
Reliability-aware controller placement for Software-Defined Networks

The Software-Defined Network (SDN) approach decouples control and forwarding planes. Such separation introduces reliability design issues of the SDN control network, since disconnection between the control and forwarding planes may lead to severe packet loss and performance degradation. This paper addresses the problem of placing controllers in SDNs, so as to maximize the reliability of control networks. After presenting a metric to characterize the reliability of SDN control networks, several placement algorithms are developed. We evaluate these algorithms and further quantify the impact of controller number on the reliability of control networks using real topologies. Our approach can significantly improve the reliability of SDN control networks without introducing unacceptable latencies.
Superhydrophobic silanized melamine sponges as high efficiency oil absorbent materials.

Superhydrophobic sponges and sponge-like materials have attracted great attention recently as potential sorbent materials for oil spill cleanup due to their excellent sorption capacity and high selectivity. A major challenge to their broad use is the fabrication of superhydrophobic sponges with superior recyclability, good mechanical strength, low cost, and manufacture scalability. In this study, we demonstrate a facile, cost-effective, and scalable method to fabricate robust, superhydrophobic sponges through the silanization of commercial melamine sponges via a solution-immersion process. The silanization was achieved through secondary amine groups on the surface of the sponge skeletons with alkylsilane compounds, forming self-assembled monolayers on the surface of sponge skeletons. This resulted in our ability to tune the surface properties of the sponges from being hydrophilic to superhydrophobic with a water contact angle of 151.0°. The superhydrophobic silanized melamine sponge exhibited excellent sorption capacity for a wide range of organic solvents and oils, from 82 to 163 times its own weight, depending on the polarity and density of the employed organic solvents and oils, and high selectivity and outstanding recyclability with an absorption capacity retention greater than 90% after 1000 cycles. These findings offer an effective approach for oil spill containment and environmental remediation.
Real-Time Detection of Camera Tampering

This paper presents a novel technique for camera tampering detection. It is implemented in real-time and was developed for use in surveillance and security applications. This method identifies camera tampering by detecting large differences between older frames of video and more recent frames. A buffer of incoming video frames is kept and three different measures of image dissimilarity are used to compare the frames. After normalization, a set of conditions is tested to decide if camera tampering has occurred. The effects of adjusting the internal parameters of the algorithm are examined. The performance of this method is shown to be extremely favorable in real-world settings.
The Architecture and Datasets of Docear's Research Paper Recommender System

In the past few years, we have developed a research paper recommender system for our reference management software Docear. In this paper, we introduce the architecture of the recommender system and four datasets. The architecture comprises of multiple components, e.g. for crawling PDFs, generating user models, and calculating content-based recommendations. It supports researchers and developers in building their own research paper recommender systems, and is, to the best of our knowledge, the most comprehensive architecture that has been released in this field. The four datasets contain metadata of 9.4 million academic articles, including 1.8 million articles publicly available on the Web; the articles’ citation network; anonymized information on 8,059 Docear users; information about the users’ 52,202 mind-maps and personal libraries; and details on the 308,146 recommendations that the recommender system delivered. The datasets are a unique source of information to enable, for instance, research on collaborative filtering, content-based filtering, and the use of referencemanagement and mind-mapping software.
MIMO Radar with Widely Separated Antennas

MIMO (multiple-input multiple-output) radar refers to an architecture that employs multiple, spatially distributed transmitters and receivers. While, in a general sense, MIMO radar can be viewed as a type of multistatic radar, the separate nomenclature suggests unique features that set MIMO radar apart from the multistatic radar literature and that have a close relation to MIMO communications. This article reviews some recent work on MIMO radar with widely separated antennas. Widely separated transmit/receive antennas capture the spatial diversity of the target's radar cross section (RCS). Unique features of MIMO radar are explained and illustrated by examples. It is shown that with noncoherent processing, a target's RCS spatial variations can be exploited to obtain a diversity gain for target detection and for estimation of various parameters, such as angle of arrival and Doppler. For target location, it is shown that coherent processing can provide a resolution far exceeding that supported by the radar's waveform.
Efficient Nonsmooth Nonconvex Optimization for Image Restoration and Segmentation

In this article, we introduce variational image restoration and segmentation models that incorporate the L1 data-fidelity measure and a nonsmooth, nonconvex regularizer. The L1 fidelity term allows us to restore or segment an image with low contrast or outliers, and the nonconvex regularizer enables homogeneous regions of the objective function (a restored image or an indicator function of a segmented region) to be efficiently smoothed while edges are well preserved. To handle the nonconvexity of the regularizer, a multistage convex relaxation method is adopted. This provides a better solution than the classical convex total variation regularizer, or than the standard L1 convex relaxation method. Furthermore, we design fast and efficient optimization algorithms that can handle the non-differentiability of both the fidelity and regularization terms. The proposed iterative algorithms asymptotically solve the original nonconvex problems. Our algorithms output a restored image or segmented regions in the image, as well as an edge indicator that characterizes the edges of the output, similar to Mumford–Shah-like regularizing functionals. Numerical examples demonstrate the promising results of the proposed restoration and segmentation models.
Serpins Promote Cancer Cell Survival and Vascular Co-Option in Brain Metastasis

Brain metastasis is an ominous complication of cancer, yet most cancer cells that infiltrate the brain die of unknown causes. Here, we identify plasmin from the reactive brain stroma as a defense against metastatic invasion, and plasminogen activator (PA) inhibitory serpins in cancer cells as a shield against this defense. Plasmin suppresses brain metastasis in two ways: by converting membrane-bound astrocytic FasL into a paracrine death signal for cancer cells, and by inactivating the axon pathfinding molecule L1CAM, which metastatic cells express for spreading along brain capillaries and for metastatic outgrowth. Brain metastatic cells from lung cancer and breast cancer express high levels of anti-PA serpins, including neuroserpin and serpin B2, to prevent plasmin generation and its metastasis-suppressive effects. By protecting cancer cells from death signals and fostering vascular co-option, anti-PA serpins provide a unifying mechanism for the initiation of brain metastasis in lung and breast cancers.
The brain as a flexible task machine: implications for visual rehabilitation using noninvasive vs. invasive approaches.

The exciting view of our brain as highly flexible task-based and not sensory-based raises the chances for visual rehabilitation, long considered unachievable, given adequate training in teaching the brain how to see. Recent advances in rehabilitation approaches, both noninvasive, like sensory substitution devices (SSDs) which present visual information using sound or touch, and invasive, like visual prosthesis, may potentially be used to achieve this goal, each alone, and most preferably together.



Visual impairments and said solutions are being used as a model for answering fundamental questions ranging from basic cognitive neuroscience, showing that several key visual brain areas are actually highly flexible, modality-independent and, as was recently shown, even visual experience-independent task machines, to technological and behavioral developments, allowing blind persons to 'see' using SSDs and other approaches.



SSDs can be potentially used as a research tool for assessing the brain's functional organization; as an aid for the blind in daily visual tasks; to visually train the brain prior to invasive procedures, by taking advantage of the 'visual' cortex's flexibility and task specialization even in the absence of vision; and to augment postsurgery functional vision using a unique SSD-prostheses hybrid. Taken together the reviewed results suggest a brighter future for visual neuro-rehabilitation.
Data-Driven Vehicle Trajectory Prediction

Vehicle trajectory or route prediction is useful in online, data-driven transportation simulation to predict future traffic patterns and congestion, among other uses. The various approaches to route prediction have varying degrees of data required to predict future vehicle trajectories. Three approaches to vehicle trajectory prediction, along with extensions, are examined to assess their accuracy on an urban road network. These include an approach based on the intuition that drivers attempt to reduce their travel time, an approach based on neural networks, and an approach based on Markov models. The T-Drive trajectory data set consisting of GPS trajectories of over ten thousand taxicabs and including 15 million data points in Beijing, China is used for this evaluation. These comparisons illustrate that using trajectory data from other vehicles can substantially improve the accuracy of forward trajectory prediction in the T-Drive data set. These results highlight the benefit of exploiting dynamic data to improve the accuracy of transportation simulation predictions.
Problems in automatic abstracting

the 1440 system is under consideration. The program can be easily adjusted to solve a large variety of menu problems witl~ different sets of objectives. Acknowledgments. The author acknowledges the cooperation of Tulane Bio-Medical Computing System and Touro Infirmary, New Orleans, on the project with special appreciation for the support of Dr. A variety of problems concerning the design and operation of an automatic abstracting system are discussed. The purpose is to present a general view of several major problem areas. No attempt is made to discuss details or to indicate preferences among alternative solutions.
MEMS Based Broadband Piezoelectric Ultrasonic Energy Harvester (PUEH) for Enabling Self-Powered Implantable Biomedical Devices.

Acoustic energy transfer is a promising energy harvesting technology candidate for implantable biomedical devices. However, it does not show competitive strength for enabling self-powered implantable biomedical devices due to two issues - large size of bulk piezoelectric ultrasound transducers and output power fluctuation with transferred distance due to standing wave. Here we report a microelectromechanical systems (MEMS) based broadband piezoelectric ultrasonic energy harvester (PUEH) to enable self-powered implantable biomedical devices. The PUEH is a microfabricated lead zirconate titanate (PZT) diaphragm array and has wide operation bandwidth. By adjusting frequency of the input ultrasound wave within the operation bandwidth, standing wave effect can be minimized for any given distances. For example, at 1 cm distance, power density can be increased from 0.59 μW/cm(2) to 3.75 μW/cm(2) at input ultrasound intensity of 1 mW/cm(2) when frequency changes from 250 to 240 kHz. Due to the difference of human body and manual surgical process, distance fluctuation for implantable biomedical devices is unavoidable and it strongly affects the coupling efficiency. This issue can be overcome by performing frequency adjustment of the PUEH. The proposed PUEH shows great potential to be integrated on an implanted biomedical device chip as power source for various applications.
PinPoint: Localizing Interfering Radios

This paper presents PinPoint, a technique for localizing rogue interfering radios that adhere to standard protocols in the inhospitable ISM band without any cooperation from the interfering radio. PinPoint is designed to be incrementally deployed on top of existing 802.11 WLAN infrastructure, and used by network administrators to identify and troubleshoot sources of interference which may be disrupting the network. PinPoint’s key contribution is a novel algorithm that accurately computes the line of sight angle of arrival (AoA) and cyclic signal strength indicator (CSSI) of the target interfering signal at all APs, even when the line of sight (LoS) component is buried by stronger multipath components, interference and noise. PinPoint leverages this algorithm to design an optimization technique, which can localize interfering radios and simultaneously identify the type of interference. Unlike several localization techniques which require extensive pre-deployment calibration (e.g. RFFingerprinting), PinPoint requires very little calibration by the network administrator, and uses a novel algorithm to self-initialize its bearings, even if the locations of some AP are initially unknown and are oriented randomly. We implement PinPoint on WARP software radios and deploy in an indoor testbed spanning an entire floor of our department. We compare PinPoint with the best known prior RSSI [8, 11] and MUSIC-AoA based approaches and show that PinPoint achieves a median localization error of 0.97 meters, which is around three times lower compared to the RSSI [8, 11] and MUSIC-AoA based approaches.
Improving retention and graduate recruitment through immersive research experiences for undergraduates

Research experiences for undergraduates are considered an effective means for increasing student retention and encouraging undergraduate students to continue on to graduate school. However, managing a cohort of undergraduate researchers, with varying skill levels, can be daunting for faculty advisors. We have developed a program to engage students in research and outreach in visualization, virtual reality, networked robotics, and interactive games. Our program immerses students into the life of a lab, employing a situated learning approach that includes tiered mentoring and collaboration to enable students at all levels to contribute to research. Students work in research teams comprised of other undergraduates, graduate students and faculty, and participate in professional development and social gatherings within the larger cohort. Results from our first two years indicate this approach is manageable and effective for increasing students' ability and desire to conduct research.
Implementing linearizability at large scale and low latency

Linearizability is the strongest form of consistency for concurrent systems, but most large-scale storage systems settle for weaker forms of consistency. RIFL provides a general-purpose mechanism for converting at-least-once RPC semantics to exactly-once semantics, thereby making it easy to turn non-linearizable operations into linearizable ones. RIFL is designed for large-scale systems and is lightweight enough to be used in low-latency environments. RIFL handles data migration by associating linearizability metadata with objects in the underlying store and migrating metadata with the corresponding objects. It uses a lease mechanism to implement garbage collection for metadata. We have implemented RIFL in the RAMCloud storage system and used it to make basic operations such as writes and atomic increments linearizable; RIFL adds only 530 ns to the 13.5 μs base latency for durable writes. We also used RIFL to construct a new multi-object transaction mechanism in RAMCloud; RIFL's facilities significantly simplified the transaction implementation. The transaction mechanism can commit simple distributed transactions in about 20 μs and it outperforms the H-Store main-memory database system for the TPC-C benchmark.
Geometric Correspondence Network for Camera Motion Estimation

In this paper, we propose a new learning scheme for generating geometric correspondences to be used for visual odometry. A convolutional neural network (CNN) combined with a recurrent neural network (RNN) are trained together to detect the location of keypoints as well as to generate corresponding descriptors in one unified structure. The network is optimized by warping points from source frame to reference frame, with a rigid body transform. Essentially, learning from warping. The overall training is focused on movements of the camera rather than movements within the image, which leads to better consistency in the matching and ultimately better motion estimation. Experimental results show that the proposed method achieves better results than both related deep learning and hand crafted methods. Furthermore, as a demonstration of the promise of our method we use a naive SLAM implementation based on these keypoints and get a performance on par with ORB-SLAM.