nils-herrmann/clean_scirepeval huggingface dataset:
C1-Continuous Terrain Reconstruction from Sparse Contours

Contour lines from topographic maps are still the most common form of elevation data for the Earth’s surface and in the case of historical landscapes, they often are the only available source of information. In this paper we present a new contour interpolation method that solves this bivariate problem by considering univariate curve interpolation along the approximate gradient directions of the unknown surface. For a point between two contours the height value is computed with Hermite interpolation based on the shortest distances to the contours and height and derivative information at the contours. The surfaces generated are C except at terrain characteristics such as ridges and valleys which are reconstructed as sharp features. The method also faithfully reconstructs summits, pits, and saddles and is especially well-suited for sparse sets of contours. The approach allows for an efficient numerical implementation as we demonstrate with a number of examples.
Morpheus: automatically generating heuristics to detect Android emulators

Emulator-based dynamic analysis has been widely deployed in Android application stores. While it has been proven effective in vetting applications on a large scale, it can be detected and evaded by recent Android malware strains that carry detection heuristics. Using such heuristics, an application can check the presence or contents of certain artifacts and infer the presence of emulators. However, there exists little work that systematically discovers those heuristics that would be eventually helpful to prevent malicious applications from bypassing emulator-based analysis. To cope with this challenge, we propose a framework called Morpheus that automatically generates such heuristics. Morpheus leverages our insight that an effective detection heuristic must exploit discrepancies observable by an application. To this end, Morpheus analyzes the application sandbox and retrieves observable artifacts from both Android emulators and real devices. Afterwards, Morpheus further analyzes the retrieved artifacts to extract and rank detection heuristics. The evaluation of our proof-of-concept implementation of Morpheus reveals more than 10,000 novel detection heuristics that can be utilized to detect existing emulator-based malware analysis tools. We also discuss the discrepancies in Android emulators and potential countermeasures.
MMap: Fast billion-scale graph computation on a PC via memory mapping

Graph computation approaches such as GraphChi and TurboGraph recently demonstrated that a single PC can perform efficient computation on billion-node graphs. To achieve high speed and scalability, they often need sophisticated data structures and memory management strategies. We propose a minimalist approach that forgoes such requirements, by leveraging the fundamental memory mapping (MMap) capability found on operating systems. We contribute: (1) a new insight that MMap is a viable technique for creating fast and scalable graph algorithms that surpasses some of the best techniques; (2) the design and implementation of popular graph algorithms for billion-scale graphs with little code, thanks to memory mapping; (3) extensive experiments on real graphs, including the 6.6 billion edge Yahoo Web graph, and show that this new approach is significantly faster or comparable to the highly-optimized methods (e.g., 9.5X faster than GraphChi for computing PageRank on 1.47B edge Twitter graph). We believe our work provides a new direction in the design and development of scalable algorithms. Our packaged code is available at http://poloclub.gatech.edu/mmap/.
Higher order autocorrelations for pattern classification

The useof higher-order local autocorrelationsas features for patternrecognitionhasbeenacknowledgedsincemany years,but their applicabilitywasrestrictedto relatively low orders(2 or 3) andsmall local neighborhoods, dueto combinatorialincreasein computational costs. In this papera new methodfor usingthesefeaturesis presented,which allows the useof autocorrelationsof any orderandof largerneighborhoods. Themethodis closelyrelatedto theclassifierused,aSupportVectorMachine(SVM), andexploits the specialform of the inner productsof autocorrelationsand the propertiesof somekernel functions usedby SVMs. Using SVM, linear andnon-linearclassification functions can be learned,extending the previous works on higher-orderautocorrelationswhich were based on linearclassifiers.
Backward Stochastic Differential Equation , Nonlinear Expectation and Their Applications

We give a survey of the developments in the theory of Backward Stochastic Differential Equations during the last 20 years, including the solutions’ existence and uniqueness, comparison theorem, nonlinear Feynman-Kac formula, g-expectation and many other important results in BSDE theory and their applications to dynamic pricing and hedging in an incomplete financial market. We also present our new framework of nonlinear expectation and its applications to financial risk measures under uncertainty of probability distributions. The generalized form of law of large numbers and central limit theorem under sublinear expectation shows that the limit distribution is a sublinear Gnormal distribution. A new type of Brownian motion, G-Brownian motion, is constructed which is a continuous stochastic process with independent and stationary increments under a sublinear expectation (or a nonlinear expectation). The corresponding robust version of Itô’s calculus turns out to be a basic tool for problems of risk measures in finance and, more general, for decision theory under uncertainty. We also discuss a type of “fully nonlinear” BSDE under nonlinear expectation.
Investigating Jordanian nurses' handwashing beliefs, attitudes, and compliance.

Low rates of handwashing compliance among nurses are still reported in literature. Handwashing beliefs and attitudes were found to correlate and predict handwashing practices. However, such an important field is not fully explored in Jordan.



This study aims at exploring Jordanian nurses' handwashing beliefs, attitudes, and compliance and examining the predictors of their handwashing compliance.



A cross-sectional multicenter survey design was used to collect data from registered nurses and nursing assistants (N = 198) who were providing care to patients in governmental hospitals in Jordan. Data collection took place over 3 months during the period of February 2011 to April 2011 using the Handwashing Assessment Inventory.



Participants' mean score of handwashing compliance was 74.29%. They showed positive attitudes but seemed to lack knowledge concerning handwashing. Analysis revealed a 5-predictor model, which accounted for 37.5% of the variance in nurses' handwashing compliance. Nurses' beliefs relatively had the highest prediction effects (β = .309, P < .01), followed by skin assessment (β = .290, P < .01).



Jordanian nurses reported moderate handwashing compliance and were found to lack knowledge concerning handwashing protocols, for which education programs are recommended. This study raised the awareness regarding the importance of complying with handwashing protocols.
Zero-voltage-switching half-bridge DC-DC converter with modified PWM control method

Asymmetric control scheme is an approach to achieve zero-voltage switching (ZVS) for half-bridge isolated dc-dc converters. However, it is not suited for wide range of input voltage due to the uneven voltage and current components stresses. This paper presents a novel "duty-cycle-shifted pulse-width modulated" (DCS PWM) control scheme for half-bridge isolated dc-dc converters to achieve ZVS operation for one of the two switches without causing the asymmetric penalties in the asymmetric control and without adding additional components. Based on the DCS PWM control scheme, an active-clamp branch comprising an auxiliary switch and a diode is added across the isolation transformer primary winding in the half-bridge converter to achieve ZVS for the other main switch by utilizing energy stored in the transformer leakage inductance. Moreover, the auxiliary switch also operates at ZVS and zero-current switching (ZCS) conditions. Furthermore, during the off-time period, the ringing resulted from the oscillation between the transformer leakage inductance and the junction capacitance of two switches is eliminated owing to the active-clamp branch and DCS PWM control scheme. Hence, switching losses and leakage-inductance-related losses are significantly reduced, which provides the converter with the potential to operate at higher efficiencies and higher switching frequencies. The principle of operation and key features of the proposed DCS PWM control scheme and two ZVS half-bridge topologies are illustrated and experimentally verified.
Using directional antennas for medium access control in ad hoc networks

Previous research in wireless ad hoc networks typically assumes the use of omnidirectional antennas at all nodes. With omnidirectional antennas, while two nodes are communicating using a given channel, MAC protocols such as IEEE 802.11 require all other nodes in the vicinity to stay silent. With directional antennas, two pairs of nodes located in each other's vicinity may potentially communicate simultaneously, depending on the directions of transmission. This can increase spatial reuse of the wireless channel. In addition, the higher gain of directional antennas allows a node to communicate with other nodes located far away, implying that messages could be delivered to the destination in fewer hops. In this paper, we propose a MAC protocol that exploits the characteristics of directional antennas. Our design focuses on using multi-hop RTSs to establish links between distant nodes, and then transmit CTS, DATA and ACK over a single hop. Results show that our directional MAC protocol can perform better than IEEE 802.11, although we find that the performance is dependent on the topology configuration and the flow patterns in the system.
Approximate storage in solid-state memories

Memories today expose an all-or-nothing correctness model that incurs significant costs in performance, energy, area, and design complexity. But not all applications need high-precision storage for all of their data structures all of the time. This article proposes mechanisms that enable applications to store data approximately and shows that doing so can improve the performance, lifetime, or density of solid-state memories. We propose two mechanisms. The first allows errors in multilevel cells by reducing the number of programming pulses used to write them. The second mechanism mitigates wear-out failures and extends memory endurance by mapping approximate data onto blocks that have exhausted their hardware error correction resources. Simulations show that reduced-precision writes in multilevel phase-change memory cells can be 1.7 × faster on average and using failed blocks can improve array lifetime by 23% on average with quality loss under 10%.
A Learning-to-Infer Method for Real-Time Power Grid Topology Identification

Identifying arbitrary topologies of power networks in real time is a computationally hard problem due to the number of hypotheses that grows exponentially with the network size. A new “Learning-to-Infer” variational inference method is developed for efficient inference of every line status in the network. Optimizing the variational model is transformed to and solved as a discriminative learning problem based on Monte Carlo samples generated with power flow simulations. A major advantage of the developed Learning-to-Infer method is that the labeled data used for training can be generated in an arbitrarily large amount fast and at very little cost. As a result, the power of offline training is fully exploited to learn very complex classifiers for effective real-time topology identification. The proposed methods are evaluated in the IEEE 30, 118 and 300 bus systems. Excellent performance in identifying arbitrary power network topologies in real time is achieved even with relatively simple variational models and a reasonably small amount of data.
Modeling Paradigms for Medical Diagnostic Decision Support: A Survey and Future Directions

Use of computer based decision tools to aid clinical decision making, has been a primary goal of research in biomedical informatics. Research in the last five decades has led to the development of Medical Decision Support (MDS) applications using a variety of modeling techniques, for a diverse range of medical decision problems. This paper surveys literature on modeling techniques for diagnostic decision support, with a focus on decision accuracy. Trends and shortcomings of research in this area are discussed and future directions are provided. The authors suggest that—(i) Improvement in the accuracy of MDS application may be possible by modeling of vague and temporal data, research on inference algorithms, integration of patient information from diverse sources and improvement in gene profiling algorithms; (ii) MDS research would be facilitated by public release of de-identified medical datasets, and development of opensource data-mining tool kits; (iii) Comparative evaluations of different modeling techniques are required to understand characteristics of the techniques, which can guide developers in choice of technique for a particular medical decision problem; and (iv) Evaluations of MDS applications in clinical setting are necessary to foster physicians’ utilization of these decision aids.
Using clustering methods to improve ontology-based query term disambiguation

In this article we describe results of our research on the disambiguation of user queries using ontologies for categorization. We present an approach to cluster search results by using classes or “Sense Folders” ~prototype categories! derived from the concepts of an assigned ontology, in our case WordNet. Using the semantic relations provided from such a resource, we can assign categories to prior, not annotated documents. The disambiguation of query terms in documents with respect to a user-specific ontology is an important issue in order to improve the retrieval performance for the user. Furthermore, we show that a clustering process can enhance the semantic classification of documents, and we discuss how this clustering process can be further enhanced using only the most descriptive classes of the ontology.
3D feature points detection on sparse and non-uniform pointcloud for SLAM

In this paper, we propose a novel 3D feature point detection algorithm using Multiresolution Surface Variation (MSV). The proposed algorithm is used to extract 3D features from a cluttered, unstructured environment for use in realtime Simultaneous Localisation and Mapping (SLAM) algorithms running on a mobile robot. The salient feature of the proposed method is that, it can not only handle dense, uniform 3D point clouds (such as those obtained from Kinect or rotating 2D Lidar), but also (perhaps more importantly) handle sparse, non-uniform 3D point clouds (obtained from sensors such as 3D Lidar) and produce robust, repeatable key points that are specifically suitable for SLAM. The efficacy of the proposed method is evaluated using a dataset collected from a mobile robot with a 3D Velodyne Lidar (VLP-16) mounted on top.
Glycosylation in cancer: mechanisms and clinical implications

Despite recent progress in understanding the cancer genome, there is still a relative delay in understanding the full aspects of the glycome and glycoproteome of cancer. Glycobiology has been instrumental in relevant discoveries in various biological and medical fields, and has contributed to the deciphering of several human diseases. Glycans are involved in fundamental molecular and cell biology processes occurring in cancer, such as cell signalling and communication, tumour cell dissociation and invasion, cell–matrix interactions, tumour angiogenesis, immune modulation and metastasis formation. The roles of glycans in cancer have been highlighted by the fact that alterations in glycosylation regulate the development and progression of cancer, serving as important biomarkers and providing a set of specific targets for therapeutic intervention. This Review discusses the role of glycans in fundamental mechanisms controlling cancer development and progression, and their applications in oncology.
Design and realization of expressway vehicle path recognition and ETC system based on RFID

With the development of expressway, the vehicle path recognition based on RFID is designed and an Electronic Toll Collection system of expressway will be implemented. It uses a passive RFID tag as carrier to identify Actual vehicle path in loop road. The ETC system will toll collection without parking, also census traffic flow and audit road maintenance fees. It is necessary to improve expressway management.
Centroid-based analysis of quadruped-robot walking balance

Multi-legged robots are very useful to do tasks in rough environment. A challenging task in their applications is to manage its walking balance. To analyze such a walking balance, we consider a simple model of quadruped robotic walking and try to identify the centroid of foot polygons formed in every step. Also, we propose a performance index to estimate the walking balance. Simulation studies show that an indispensable waddling motion of the quadruped walking can be estimated by the robot's centroid trajectory and the proposed balance index. The walking balance of the robot depends on the walking pattern employed. Finally, we discuss on a strategy to maintain the best walking balance and to facilitate an excessive dynamic walking motion. A useful walking style for the quadruped robot is also addressed in the bio-mimetic aspect.
Effect of mineral oil, sunflower oil, and coconut oil on prevention of hair damage.

Previously published results showed that both in vitro and in vivo coconut oil (CNO) treatments prevented combing damage of various hair types. Using the same methodology, an attempt was made to study the properties of mineral oil and sunflower oil on hair. Mineral oil (MO) was selected because it is extensively used in hair oil formulations in India, because it is non-greasy in nature, and because it is cheaper than vegetable oils like coconut and sunflower oils. The study was extended to sunflower oil (SFO) because it is the second most utilized base oil in the hair oil industry on account of its non-freezing property and its odorlessness at ambient temperature. As the aim was to cover different treatments, and the effect of these treatments on various hair types using the above oils, the number of experiments to be conducted was a very high number and a technique termed as the Taguchi Design of Experimentation was used. The findings clearly indicate the strong impact that coconut oil application has to hair as compared to application of both sunflower and mineral oils. Among three oils, coconut oil was the only oil found to reduce the protein loss remarkably for both undamaged and damaged hair when used as a pre-wash and post-wash grooming product. Both sunflower and mineral oils do not help at all in reducing the protein loss from hair. This difference in results could arise from the composition of each of these oils. Coconut oil, being a triglyceride of lauric acid (principal fatty acid), has a high affinity for hair proteins and, because of its low molecular weight and straight linear chain, is able to penetrate inside the hair shaft. Mineral oil, being a hydrocarbon, has no affinity for proteins and therefore is not able to penetrate and yield better results. In the case of sunflower oil, although it is a triglyceride of linoleic acid, because of its bulky structure due to the presence of double bonds, it does not penetrate the fiber, consequently resulting in no favorable impact on protein loss.
Rapid Destruction of the Humeral Head Caused by Subchondral Insufficiency Fracture: A Report of Two Cases

Rapidly destructive arthritis (RDA) of the shoulder is a rare disease. Here, we report two cases, with different destruction patterns, which were most probably due to subchondral insufficiency fractures (SIFs). Case 1 involved a 77-year-old woman with right shoulder pain. Rapid destruction of both the humeral head and glenoid was seen within 1 month of the onset of shoulder pain. We diagnosed shoulder RDA and performed a hemiarthroplasty. Case 2 involved a 74-year-old woman with left shoulder pain. Humeral head collapse was seen within 5 months of pain onset, without glenoid destruction. Magnetic resonance imaging showed a bone marrow edema pattern with an associated subchondral low-intensity band, typical of SIF. Total shoulder arthroplasty was performed in this case. Shoulder RDA occurs as a result of SIF in elderly women; the progression of the joint destruction is more rapid in cases with SIFs of both the humeral head and the glenoid. Although shoulder RDA is rare, this disease should be included in the differential diagnosis of acute onset shoulder pain in elderly female patients with osteoporosis and persistent joint effusion.
An Integrated Web-based Interactive Data Platform for Molecular Dynamics Simulations

The article aims to introduce an integrated web-based interactive data platform for molecular dynamic simulations using the datasets generated by different life science communities from Armenia. The suggested platform, consisting of data repository and workflow management services, is vital for current and future scientific discoveries in the life science domain. We focus on interactive data visualization workflow service as a key to perform more in-depth analyzes of research data outputs, helping to understand the problems efficiently and to consolidate the data into one collective illustration platform. The functionalities of the integrated data platform is presented as an advanced integrated environment to capture, analyze, process and visualize the scientific data.
BayesRank: A Bayesian Approach to Ranked Peer Grading

Advances in online and computer supported education afford exciting opportunities to revolutionize the classroom, while also presenting a number of new challenges not faced in traditional educational settings. Foremost among these challenges is the problem of accurately and efficiently evaluating learner work as the class size grows, which is directly related to the larger goal of providing quality, timely, and actionable formative feedback. Recently there has been a surge in interest in using peer grading methods coupled with machine learning to accurately and fairly evaluate learner work while alleviating the instructor bottleneck and grading overload. Prior work in peer grading almost exclusively focuses on numerically scored grades -- either real-valued or ordinal. In this work, we consider the implications of peer ranking in which learners rank a small subset of peer work from strongest to weakest, and propose new types of computational analyses that can be applied to this ranking data. We adopt a Bayesian approach to the ranked peer grading problem and develop a novel model and method for utilizing ranked peer-grading data. We additionally develop a novel procedure for adaptively identifying which work should be ranked by particular peers in order to dynamically resolve ambiguity in the data and rapidly resolve a clearer picture of learner performance. We showcase our results on both synthetic and several real-world educational datasets.
Electromagnetic Inspection of Carbon-Fiber- Reinforced Polymer Composites With Coupled Spiral Inductors

This paper presents a new type of an electromagnetic sensor for nondestructive testing of carbon-fiber-reinforced polymer composites. The sensor utilizes coupled planar spiral inductors operating typically in the range of 10-500 MHz. The method proposed here shows some similarity to the eddy current technique, but as will be shown, the principles of operation are different as the sensitivity to defects is mostly due to the magnetic field components tangential to the surface of a material. It is shown that the method is applicable to 3-D inspection of carbon-fiber-reinforced composites widely employed in the aerospace industry.