nils-herrmann/clean_scirepeval huggingface dataset:
Crowdsourcing Linked Open Data for Disaster Management

This paper shows how Linked Open Data can ease the challenges of information triage in disaster response efforts. Recently, disaster management has seen a revolution in data collection. Local victims as well as people all over the world collect observations and make them available on the web. Yet, this crucial and timely information source comes unstructured. This hinders a processing and integration, and often a general consideration of this information. Linked Open Data is supported by number of freely available technologies, backed up by a large community in academia and it offers the opportunity to create flexible mash-up solutions. At hand of the Ushahidi Haiti platform, this paper suggests crowdsourced Linked Open Data. We take a look at the requirements, the tools that are there to meet these requirements, and suggest an architecture to enable non-experts to contribute Linked Open Data.
A fully on-chip 25MHz PVT-compensation CMOS Relaxation Oscillator

A fully on-chip, low-power and small area CMOS Relaxation Oscillator (ROSC) with voltage integral feedback structure and a new Bandgap Reference voltage (BGR) for accurate oscillation frequency independent of the PVT and comparator's delay variations is presented. The designed circuit uses a new bandgap reference to generate the reference voltage required by relaxation oscillator which allow variations due to voltage and temperature to be compensated. Another merit of this oscillator is that the phase noise at low-offset frequency is suppressed by the voltage integral feedback circuit. The frequency of the relaxation oscillator is determined by the RC response time. Thus, the current and capacitance are controlled by temperature and process compensation circuits to compensate for the frequency variation. The ROSC is implemented in a 0.18μm CMOS technology and its active area is 0.14mm2. The target frequency is 25MHz and current consumption is 22μA, where VDD is 1.8V. The oscillation frequency variation for VDD ranges from 1.4 to 1.9V is 0.2% and for temperature ranges from -40 to 125°C is 0.18%.
Cultural Differences in Spontaneous Trait and Situation Inferences

Previous findings indicated that when people observe someone’s behavior, they spontaneously infer the traits and situations that cause the target person’s behavior. These inference processes are called spontaneous trait inferences (STIs) and spontaneous situation inferences (SSIs). While both patterns of inferences have been observed, no research has examined the extent to which people from different cultural backgrounds produce these inferences when information affords both trait and situation inferences. Based on the theoretical frameworks of social orientations and thinking styles, we hypothesized that European Canadians would be more likely to produce STIs than SSIs because of the individualistic/independent social orientation and the analytic thinking style dominant in North America, whereas Japanese would produce both STIs and SSIs equally because of the collectivistic/interdependent social orientation and the holistic thinking style dominant in East Asia. Employing the savings-in-relearning paradigm, we presented information that affords both STIs and SSIs and examined cultural differences in the extent of both inferences. The results supported our hypotheses. The relationships between culturally dominant styles of thought and the inference processes in impression formation are discussed.
Is Genicular Nerve Radiofrequency Ablation Safe? A Literature Review and Anatomical Study.

Genicular nerve radiofrequency ablation (RFA) has recently gained popularity as an intervention for chronic knee pain in patients who have failed other conservative or surgical treatments. Long-term efficacy and adverse events are still largely unknown. Under fluoroscopic guidance, thermal RFA targets the lateral superior, medial superior, and medial inferior genicular nerves, which run in close proximity to the genicular arteries that play a crucial role in supplying the distal femur, knee joint, meniscus, and patella. RFA targets nerves by relying on bony landmarks, but fails to provide visualization of vascular structures. Although vascular injuries after genicular nerve RFA have not been reported, genicular vascular complications are well documented in the surgical literature. This article describes the anatomy, including detailed cadaveric dissections and schematic drawings, of the genicular neurovascular bundle. The present investigation also included a comprehensive literature review of genicular vascular injuries involving those arteries which lie near the targets of genicular nerve RFA. These adverse vascular events are documented in the literature as case reports. Of the 27 cases analyzed, 25.9% (7/27) involved the lateral superior genicular artery, 40.7% (11/27) involved the medial superior genicular artery, and 33.3% (9/27) involved the medial inferior genicular artery. Most often, these vascular injuries result in the formation of pseudoaneurysm, arteriovenous fistula (AVF), hemarthrosis, and/or osteonecrosis of the patella. Although rare, these complications carry significant morbidities. Based on the detailed dissections and review of the literature, our investigation suggests that vascular injury is a possible risk of genicular RFA. Lastly, recommendations are offered to minimize potential iatrogenic complications.
Virtual machine security challenges: case studies

Currently Virtual Machines (VMs) have many applications and their use is growing constantly as the hardware gets more powerful and usage more regulated allowing for scaling, monitoring, portability, security applications and many other uses. There are many types of virtualization techniques that can be employed on many levels from simple sandbox to full fledged streamlined managed access. While scaling, software lifecycles and diversity are just some of security challenges faced by VM developers the failure to properly implement those mechanisms may lead to VM escape, host access, denial of service and more. There are many exploits found in the last couple of years which were fixed on latest versions but some systems are still running them and vulnerable as presented, mostly to host based attacks and some have dramatic consequences.
Marine Geospatial Ecology Tools: An integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++

With the arrival of GPS, satellite remote sensing, and personal computers, the last two decades have witnessed rapid advances in the field of spatially-explicit marine ecological modeling. But with this innovation has come complexity. To keep up, ecologists must master multiple specialized software packages, such as ArcGIS for display and manipulation of geospatial data, R for statistical analysis, and MATLAB for matrix processing. This requires a costly investment of time and energy learning computer programming, a high hurdle for many ecologists. To provide easier access to advanced analytic methods, we developed Marine Geospatial Ecology Tools (MGET), an extensible collection of powerful, easy-to-use, open-source geoprocessing tools that ecologists can invoke from ArcGIS without resorting to computer programming. Internally, MGET integrates Python, R, MATLAB, and Cþþ, bringing the power of these specialized platforms to tool developers without requiring developers to orchestrate the interoperability
A $Ka$ -Band High-Gain Circularly Polarized Microstrip Antenna Array

In this letter, a four-element high-gain circularly polarized microstrip antenna array operating in the Ka -band is proposed. The sequentially rotated series-parallel stub technique is employed in constructing the feed network to allow symmetrical positioning of microstrip patch elements, resulting in good axial ratio of polarization. In addition, an L-shaped matching branch is used to offer good impedance matching and spatial flexibility for positioning patch elements. Both simulation and measurement results are presented. They agree with each other well and demonstrate much better 3-dB axial-ratio bandwidth and return loss than the single patch antenna.
Compressed Sensing with Coherent and Redundant Dictionaries

This article presents novel results concerning the recovery of signals from undersampled data in the common situation where such signals are not sparse in an orthonormal basis or incoherent dictionary, but in a truly redundant dictionary. This work thus bridges a gap in the literature and shows not only that compressed sensing is viable in this context, but also that accurate recovery is possible via an `1-analysis optimization problem. We introduce a condition on the measurement/sensing matrix, which is a natural generalization of the now well-known restricted isometry property, and which guarantees accurate recovery of signals that are nearly sparse in (possibly) highly overcomplete and coherent dictionaries. This condition imposes no incoherence restriction on the dictionary and our results may be the first of this kind. We discuss practical examples and the implications of our results on those applications, and complement our study by demonstrating the potential of `1-analysis for such problems.
A Survey of Non-Orthogonal Multiple Access for 5G

In the fifth generation (5G) of wireless communication systems, hitherto unprecedented requirements are expected to be satisfied. As one of the promising techniques of addressing these challenges, non-orthogonal multiple access (NOMA) has been actively investigated in recent years. In contrast to the family of conventional orthogonal multiple access (OMA) schemes, the key distinguishing feature of NOMA is to support a higher number of users than the number of orthogonal resource slots with the aid of non-orthogonal resource allocation. This may be realized by the sophisticated inter-user interference cancellation at the cost of an increased receiver complexity. In this paper, we provide a comprehensive survey of the original birth, the most recent development, and the future research directions of NOMA. Specifically, the basic principle of NOMA will be introduced at first, with the comparison between NOMA and OMA especially from the perspective of information theory. Then, the prominent NOMA schemes are discussed by dividing them into two categories, namely, power-domain and code-domain NOMA. Their design principles and key features will be discussed in detail, and a systematic comparison of these NOMA schemes will be summarized in terms of their spectral efficiency, system performance, receiver complexity, etc. Finally, we will highlight a range of challenging open problems that should be solved for NOMA, along with corresponding opportunities and future research trends to address these challenges.
Lane-Change Detection Based on Vehicle-Trajectory Prediction

We propose a new detection method to predict a vehicle's trajectory and use it for detecting lane changes of surrounding vehicles. According to the previous research, more than 90% of the car crashes are caused by human errors, and lane changes are the main factor. Therefore, if a lane change can be detected before a vehicle crosses the centerline, accident rates will decrease. Previously reported detection methods have the problem of frequent false alarms caused by zigzag driving that can result in user distrust in driving safety support systems. Most cases of zigzag driving are caused by the abortion of a lane change due to the presence of adjacent vehicles on the next lane. Our approach reduces false alarms by considering the possibility of a crash with adjacent vehicles by applying trajectory prediction when the target vehicle attempts to change a lane, and it reflects the result of lane-change detection. We used a traffic dataset with more than 500 lane changes and confirmed that the proposed method can considerably improve the detection performance.
DropMax: Adaptive Variational Softmax

We propose DropMax, a stochastic version of softmax classifier which at each iteration drops non-target classes according to dropout probabilities adaptively decided for each instance. Specifically, we overlay binary masking variables over class output probabilities, which are input-adaptively learned via variational inference. This stochastic regularization has an effect of building an ensemble classifier out of exponentially many classifiers with different decision boundaries. Moreover, the learning of dropout rates for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes. We validate our model on multiple public datasets for classification, on which it obtains significantly improved accuracy over the regular softmax classifier and other baselines. Further analysis of the learned dropout probabilities shows that our model indeed selects confusing classes more often when it performs classification.
Hot & tight: exploring thermo and squeeze cues recognition on wrist wearables

Wrist worn wearable computing devices are ideally suited for presenting notifications through haptic stimuli as they are always in direct contact with the user's skin. While prior work has explored the feasibility of haptic notifications, we highlight a lack of empirical studies on thermal and pressure feedback in the context of wearable devices. This paper introduces prototypes for thermal and pressure (squeeze) feedback on the wrist. It then presents a study characterizing recognition performance with thermal and pressure cues against baseline performance with vibrations.
Effect of a gel containing pilocarpine on vaginal atrophy in castrated rats

To evaluate the effect of Carbopol gel formulations containing pilocarpine on the morphology and morphometry of the vaginal epithelium of castrated rats.



Thirty-one female Wistar-Hannover rats were randomly divided into four groups: the control Groups I (n=7, rats in persistent estrus; positive controls) and II (n=7, castrated rats, negative controls) and the experimental Groups, III (n=8) and IV (n=9). Persistent estrus (Group I) was achieved with a subcutaneous injection of testosterone propionate on the second postnatal day. At 90 days postnatal, rats in Groups II, III and IV were castrated and treated vaginally for 14 days with Carbopol gel (vehicle alone) or Carbopol gel containing 5% and 15% pilocarpine, respectively. Next, all of the animals were euthanized and their vaginas were removed for histological evaluation. A non-parametric test with a weighted linear regression model was used for data analysis (p<0.05).



The morphological evaluation showed maturation of the vaginal epithelium with keratinization in Group I, whereas signs of vaginal atrophy were present in the rats of the other groups. Morphometric examinations showed mean thickness values of the vaginal epithelium of 195.10±12.23 μm, 30.90±1.14 μm, 28.16±2.98 μm and 29.84±2.30 μm in Groups I, II, III and IV, respectively, with statistically significant differences between Group I and the other three groups (p<0.0001) and no differences between Groups II, III and IV (p=0.0809).



Topical gel formulations containing pilocarpine had no effect on atrophy of the vaginal epithelium in the castrated female rats.
2016 Annual Report of The Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry.

The Society of Thoracic Surgeons (STS)/American College of Cardiology Transcatheter Valve Therapy (TVT) Registry captures all procedures with Food and Drug Administration-approved transcatheter valve devices performed in the United States, and is mandated as a condition of reimbursement by the Centers for Medicaid & Medicare Services.



This annual report focuses on patient characteristics, trends, and outcomes of transcatheter aortic and mitral valve catheter-based valve procedures in the United States.



We reviewed data for all patients receiving commercially approved devices from 2012 through December 31, 2015, that are entered in the TVT Registry.



The 54,782 patients with transcatheter aortic valve replacement demonstrated decreases in expected risk of 30-day operative mortality (STS Predicted Risk of Mortality [PROM]) of 7% to 6% and transcatheter aortic valve replacement PROM (TVT PROM) of 4% to 3% (both p < 0.0001) from 2012 to 2015. Observed in-hospital mortality decreased from 5.7% to 2.9%, and 1-year mortality decreased from 25.8% to 21.6%. However, 30-day post-procedure pacemaker insertion increased from 8.8% in 2013 to 12.0% in 2015. The 2,556 patients who underwent transcatheter mitral leaflet clip in 2015 were similar to patients from 2013 to 2014, with hospital mortality of 2% and with mitral regurgitation reduced to grade ≤2 in 87% of patients (p < 0.0001). The 349 patients who underwent mitral valve-in-valve and mitral valve-in-ring procedures were high risk, with an STS PROM for mitral valve replacement of 11%. The observed hospital mortality was 7.2%, and 30-day post-procedure mortality was 8.5%.



The TVT Registry is an innovative registry that that monitors quality, patient safety and trends for these rapidly evolving new technologies.
Chapter 2 Data Mining Methods for Recommender Systems

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.
Model selection and parameter inference in phylogenetics using Nested Sampling.

Bayesian inference methods rely on numerical algorithms for both model selection and parameter inference. In general, these algorithms require a high computational eﬀort to yield reliable estimates. One of the major challenges in phylogenetics is the estimation of the marginal likelihood. This quantity is commonly used for comparing diﬀerent evolutionary models, but its calculation, even for simple models, incurs high computational cost. Another interesting challenge relates to the estimation of the posterior distribution. Often, long Markov chains are required to get suﬃcient samples to carry out parameter inference, especially for tree distributions. In general, these problems are addressed separately by using diﬀerent procedures. Nested sampling (NS) is a Bayesian computation algorithm which provides the means to estimate marginal likelihoods together with their uncertainties, and to sample from the posterior distribution at no extra cost. The methods currently used in phylogenetics for marginal likelihood estimation lack in practicality due to their dependence on many tuning parameters and their inability of most implementations to provide a direct way to calculate the uncertainties associated with the estimates, unlike NS. In this paper, we introduce NS to phylogenetics. Its performance is analysed under diﬀerent scenarios and compared to established methods. We conclude that NS is a competitive and attractive algorithm for phylogenetic inference. An implementation is available as a package for BEAST 2 under the LGPL licence, accessible at https://github.com/BEAST2-Dev/nested-sampling.
Classified Stable Matching

We introduce the classified stable matching problem, a problem motivated by academic hiring. Suppose that a number of institutes are hiring faculty members from a pool of applicants. Both institutes and applicants have preferences over the other side. An institute classifies the applicants based on their research areas (or any other criterion), and, for each class, it sets a lower bound and an upper bound on the number of applicants it would hire in that class. The objective is to find a stable matching from which no group of participants has reason to deviate. Moreover, the matching should respect the upper/lower bounds of the classes. In the first part of the paper, we study classified stable matching problems whose classifications belong to a fixed set of "order types." We show that if the set consists entirely of downward forests, there is a polynomial-time algorithm; otherwise, it is NP-complete to decide the existence of a stable matching.
 In the second part, we investigate the problem using a polyhedral approach. Suppose that all classifications are laminar families and there is no lower bound. We propose a set of linear inequalities to describe stable matching polytope and prove that it is integral. This integrality result allows us to find optimal stable matchings in polynomial time using Ellipsoid algorithm; furthermore, it gives a description of the stable matching polytope for the many-to-many (unclassified) stable matching problem, thereby answering an open question posed by Sethuraman, Teo and Qian.
An Interpretation of Objects and Object Types

We present an interpretation of typed object-oriented concepts in terms of well-understood, purely procedural concepts. More precisely, we give a compositional subtype-preserving translation of a basic object calculus supporting method invocation, functional method update, and subtyping, into the polymorphic ¿-calculus with recursive types and subtyping. The translation techniques apply also to an imperative version of the object calculus which includes in-place method update and object cloning. Finally, the translation easily extends to "Self types" and other interesting object-oriented constructs.
UAV-Based IoT Platform: A Crowd Surveillance Use Case

Unmanned aerial vehicles are gaining a lot of popularity among an ever growing community of amateurs as well as service providers. Emerging technologies, such as LTE 4G/5G networks and mobile edge computing, will widen the use case scenarios of UAVs. In this article, we discuss the potential of UAVs, equipped with IoT devices, in delivering IoT services from great heights. A high-level view of a UAV-based integrative IoT platform for the delivery of IoT services from large height, along with the overall system orchestrator, is presented in this article. As an envisioned use case of the platform, the article demonstrates how UAVs can be used for crowd surveillance based on face recognition. To evaluate the use case, we study the offloading of video data processing to a MEC node compared to the local processing of video data onboard UAVs. For this, we developed a testbed consisting of a local processing node and one MEC node. To perform face recognition, the Local Binary Pattern Histogram method from the Open Source Computer Vision is used. The obtained results demonstrate the efficiency of the MEC-based offloading approach in saving the scarce energy of UAVs, reducing the processing time of recognition, and promptly detecting suspicious persons.
Mining Domain-Specific Thesauri from Wikipedia: A Case Study

Domain-specific thesauri are high-cost, high-maintenance, high-value knowledge structures. We show how the classic thesaurus structure of terms and links can be mined automatically from Wikipedia. In a comparison with a professional thesaurus for agriculture we find that Wikipedia contains a substantial proportion of its concepts and semantic relations; furthermore it has impressive coverage of contemporary documents in the domain. Thesauri derived using our techniques capitalize on existing public efforts and tend to reflect contemporary language usage better than their costly, painstakingly-constructed manual counterparts