nils-herrmann/clean_scirepeval huggingface dataset:
Visual Methods for Analyzing Probabilistic Classification Data

Multi-class classifiers often compute scores for the classification samples describing probabilities to belong to different classes. In order to improve the performance of such classifiers, machine learning experts need to analyze classification results for a large number of labeled samples to find possible reasons for incorrect classification. Confusion matrices are widely used for this purpose. However, they provide no information about classification scores and features computed for the samples. We propose a set of integrated visual methods for analyzing the performance of probabilistic classifiers. Our methods provide insight into different aspects of the classification results for a large number of samples. One visualization emphasizes at which probabilities these samples were classified and how these probabilities correlate with classification error in terms of false positives and false negatives. Another view emphasizes the features of these samples and ranks them by their separation power between selected true and false classifications. We demonstrate the insight gained using our technique in a benchmarking classification dataset, and show how it enables improving classification performance by interactively defining and evaluating post-classification rules.
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector

Large volumes of event data are becoming increasingly available in a wide variety of applications, such as healthcare analytics, smart cities and social network analysis. The precise time interval or the exact distance between two events carries a great deal of information about the dynamics of the underlying systems. These characteristics make such data fundamentally different from independently and identically distributed data and time-series data where time and space are treated as indexes rather than random variables. Marked temporal point processes are the mathematical framework for modeling event data with covariates. However, typical point process models often make strong assumptions about the generative processes of the event data, which may or may not reflect the reality, and the specifically fixed parametric assumptions also have restricted the expressive power of the respective processes. Can we obtain a more expressive model of marked temporal point processes? How can we learn such a model from massive data?
 In this paper, we propose the Recurrent Marked Temporal Point Process (RMTPP) to simultaneously model the event timings and the markers. The key idea of our approach is to view the intensity function of a temporal point process as a nonlinear function of the history, and use a recurrent neural network to automatically learn a representation of influences from the event history. We develop an efficient stochastic gradient algorithm for learning the model parameters which can readily scale up to millions of events. Using both synthetic and real world datasets, we show that, in the case where the true models have parametric specifications, RMTPP can learn the dynamics of such models without the need to know the actual parametric forms; and in the case where the true models are unknown, RMTPP can also learn the dynamics and achieve better predictive performance than other parametric alternatives based on particular prior assumptions.
Explaining International Migration in the Skype Network: The Role of Social Network Features

In recent years, several new ways have appeared for quantifying human migration such as location based smartphone applications and tracking user activity from websites. To show usefulness of these new approaches, we present the results of a study of cross-country migration as observed via login events in the Skype network. We explore possibility to extract human migration and correlate it with institutional statistics. The study demonstrates that a number of social network features are strongly related to net migration from and to a given country, as well as net migration between pairs of countries. Specifically, we find that percentage of international calls, percentage of international links and foreign logins in a country, complemented by gross domestic product, can be used as relatively accurate proxies for estimating migration.
Noise Modeling for RF CMOS Circuit Simulation

The RF noise in 0.18m CMOS technology has been measured and modeled. In contrast to some other groups, we find only a moderate enhancement of the drain current noise for shortchannel MOSFETs. The gate current noise on the other hand is more significantly enhanced, which is explained by the effects of the gate resistance. The experimental results are modeled with a nonquasi-static RF model, based on channel segmentation, which is capable of predicting both drain and gate current noise accurately. Experimental evidence is shown for two additional noise mechanisms: 1) avalanche noise associated with the avalanche current from drain to bulk and 2) shot noise in the direct-tunneling gate leakage current. Additionally, we show low-frequency noise measurements, which strongly point toward an explanation of the1 noise based on carrier trapping, not only in n-channel MOSFETs, but also in p-channel MOSFETs.
Augmented Fuzzy Cognitive Maps Supplemented with Case Based Reasoning for Advanced Medical Decision Support

Fuzzy Cognitive Maps (FCMs) have been used to design Decision Support Systems and particularly for medical informatics to develop Intelligent Diagnosis Systems. Even though they have been successfully used in many dif­ ferent areas, there are situations where incomplete and vague input information may present difficulty in reaching a decision. In this chapter the idea of using the Case Based Reasoning technique to augment FCMs is presented leading to the de­ velopment of an Advanced Medical Decision Support System. This system is ap­ plied in the speech pathology area to diagnose language impairments..
Markov Chain Monte Carlo with People

Many formal models of cognition implicitly use subjective probability distributions to capture the assumptions of human learners. Most applications of these models determine these distributions indirectly. We propose a method for directly determining the assumptions of human learners by sampling from subjective probability distributions. Using a correspondence between a model of human choice and Markov chain Monte Carlo (MCMC), we describe a method for sampling from the distributions over objects that people associate with different categories. In our task, subjects choose whether to accept or reject a proposed change to an object. The task is constructed so that these decisions follow an MCMC acceptance rule, defining a Markov chain for which the stationary distribution is the category distribution. We test this procedure for both artificial categories acquired in the laboratory, and natural categories acquired from experience.
A systematic review of implementation strategies for assessment, prevention, and management of ICU delirium and their effect on clinical outcomes

Despite recommendations from professional societies and patient safety organizations, the majority of ICU patients worldwide are not routinely monitored for delirium, thus preventing timely prevention and management. The purpose of this systematic review is to summarize what types of implementation strategies have been tested to improve ICU clinicians' ability to effectively assess, prevent and treat delirium and to evaluate the effect of these strategies on clinical outcomes.



We searched PubMed, Embase, PsychINFO, Cochrane and CINAHL (January 2000 and April 2014) for studies on implementation strategies that included delirium-oriented interventions in adult ICU patients. Studies were suitable for inclusion if implementation strategies' efficacy, in terms of a clinical outcome, or process outcome was described.



We included 21 studies, all including process measures, while 9 reported both process measures and clinical outcomes. Some individual strategies such as "audit and feedback" and "tailored interventions" may be important to establish clinical outcome improvements, but otherwise robust data on effectiveness of specific implementation strategies were scarce. Successful implementation interventions were frequently reported to change process measures, such as improvements in adherence to delirium screening with up to 92%, but relating process measures to outcome changes was generally not possible. In meta-analyses, reduced mortality and ICU length of stay reduction were statistically more likely with implementation programs that employed more (six or more) rather than less implementation strategies and when a framework was used that either integrated current evidence on pain, agitation and delirium management (PAD) or when a strategy of early awakening, breathing, delirium screening and early exercise (ABCDE bundle) was employed. Using implementation strategies aimed at organizational change, next to behavioral change, was also associated with reduced mortality.



Our findings may indicate that multi-component implementation programs with a higher number of strategies targeting ICU delirium assessment, prevention and treatment and integrated within PAD or ABCDE bundle have the potential to improve clinical outcomes. However, prospective confirmation of these findings is needed to inform the most effective implementation practice with regard to integrated delirium management and such research should clearly delineate effective practice change from improvements in clinical outcomes.
Realization of a Filter with Helical Components *

In the VHF range, high-quality narrow-band filters with reasonable physical dimensions are extremely d8icult to realize. Excessive pass-band insertion loss accompanies filters employing lumped constant elements, and unreasonable size is a natural consequence of coaxial-resonator filters. Harmonic crystal filters are inadequate because of the unpredictable amount of spurious modes above the harmonic frequency; and hence they can only be used for very low percentage bandwidths. A solution to the above problems is provided by the use of helical resonators for high-quality filters. This paper describes the helical resonator, measurement of its Q, and coupling to the resonator. A procedure for the design and construction of a filter using helical circuits is then presented. Finally an example is given illustrating the design method, and several photographs of helical-resonator filters are shown.
Multi-Level Topical Text Categorization with Wikipedia

This paper introduces an automatic categorical-marking model for text categorization. Traditional classification algorithms are generally applying labeled training set and call for a lot of manual work to tag classifications beforehand. Also due to the ambiguity and fuzziness of texts, the results of traditional text categorization algorithms may not be clear enough and abundant in content. This paper presents an unsupervised, training-set-free and hierarchical categorization model called Folk-Topical Text Categorization (FTTC). FTTC applies topic model to abstract documents to topical words and make use of Wikipedia's crowd-sourcing and collective control to extend hierarchical classifications. The results are not restricted to predefined categories but contain categories abstracted to deeper semantic levels and greatly facilitate traditional text categorization applications. For a document, its topical words are obtained using a popular topic model called Latent Dirichlet Allocation (LDA). Afterwards, the topical words are used to build and trace through the category-trees of Wikipedia. Based on the filtered results, the final classifications comprehensively reflect the diversified and content-rich information of the text, and fully cover different aspects of the text. Experimental results on different kinds of datasets show that our model advances in classification accuracy, flexibility and intelligibility, as compared with traditional models.
What's mine is yours: Pretrained CNNs for limited training sonar ATR

Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors. Unfortunately, the lack of enormous Sonar image data sets has prevented automatic target recognition (ATR) algorithms from some of the same advances seen in other computer vision fields. Namely, the boom in convolutional neural nets (CNNs) which have been able to achieve incredible results — even surpassing human actors — has not been an easily feasible route for many practitioners of Sonar ATR. We demonstrate the power of one avenue to incorporating CNNs into Sonar ATR: transfer learning. We first show how well a straightforward, flexible CNN feature-extraction strategy can be used to obtain impressive if not state-of-the-art results. Secondly, we propose a way to utilize the powerful transfer learning approach towards multiple instance target detection and identification within a provided synthetic aperture Sonar data set.
Indirectly Encoding Neural Plasticity as a Pattern of Local Rules

Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve the ability to adapt is by encoding local learning rules. However, a significant problem with most such approaches is that local learning rules for every connection in the network must be discovered separately. This paper aims to show that learning rules can be effectively indirectly encoded by extending the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) method. Adaptive HyperNEAT is introduced to allow not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary learning rules. Several such adaptive models with different levels of generality are explored and compared. The long-term promise of the new approach is to evolve large-scale adaptive ANNs, which is a major goal for neuroevolution.
Extracting implicit features in online customer reviews for opinion mining

As the number of customer reviews grows very rapidly, it is essential to summarize useful opinions for buyers, sellers and producers. One key step of opinion mining is feature extraction. Most existing research focus on finding explicit features, only a few attempts have been made to extract implicit features. Nearly all existing research only concentrate on product features, few has paid attention to other features that relates to sellers, services and logistics. Therefore in this paper, we propose a novel co-occurrence association-based method, which aims to extract implicit features in customer reviews and provide more comprehensive and fine-grained mining results.
A Generative Approach to Audio-Visual Person Tracking

This paper focuses on the integration of acoustic and visual information for people tracking. The system presented relies on a probabilistic framework within which information from multiple sources is integrated at an intermediate stage. An advantage of the method proposed is that of using a generative approach which supports easy and robust integration of multi source information by means of sampled projection instead of triangulation. The system described has been developed in the EU funded CHIL Project research activities. Experimental results from the CLEAR evaluation workshop are reported.
Coupled Compound Poisson Factorization

We present a general framework, the coupled compound Poisson factorization (CCPF), to capture the missing-data mechanism in extremely sparse data sets by coupling a hierarchical Poisson factorization with an arbitrary data-generating model. We derive a stochastic variational inference algorithm for the resulting model and, as examples of our framework, implement three different data-generating models—a mixture model, linear regression, and factor analysis—to robustly model non-random missing data in the context of clustering, prediction, and matrix factorization. In all three cases, we test our framework against models that ignore the missing-data mechanism on large scale studies with non-random missing data, and we show that explicitly modeling the missing-data mechanism substantially improves the quality of the results, as measured using data log likelihood on a held-out test set.
Dissecting Contributions of Prefrontal Cortex and Fusiform Face Area to Face Working Memory

Interactions between prefrontal cortex (PFC) and stimulusspecific visual cortical association areas are hypothesized to mediate visual working memory in behaving monkeys. To clarify the roles for homologous regions in humans, event-related fMRI was used to assess neural activity in PFC and fusiform face area (FFA) of subjects performing a delay-recognition task for faces. In both PFC and FFA, activity increased parametrically with memory load during encoding and maintenance of face stimuli, despite quantitative differences in the magnitude of activation. Moreover, timing differences in PFC and FFA activation during memory encoding and retrieval implied a context dependence in the flow of neural information. These results support existing neurophysiological models of visual working memory developed in the nonhuman primate.
Conversion of Natural Language Query to SQL Query

This paper present an approach to automate the conversion of Natural Language Query to SQL Query effectively. Structured Query Language is a powerful tool for managing data held in a relational database management system. To retrieve or manage data user have to enter the correct SQL Query. But the users who don't have any knowledge about SQL are unable to retrieve the required data. To overcome this we proposed a model in Natural Language Processing for converting the Natural Language Query to SQL query. This helps novice user to get required content without knowing any complex details about SQL. This system can also deal with complex queries. This system is designed for Training and Placement cell officers who work on student database but don't have any knowledge about SQL. In this system, user can also enter the query using speech. System will convert speech into the text format. This query will get transformed to SQL query. System will execute the query and gives output to the user.
Contactless Slipring System based on rotating magnetic field principle for rotary applications

This paper presents a Contactless Slipring System (CSS) based on rotating magnetic field theory for transferring electrical power to a rotatable shaft. Besides the analytical expressions, a 3-D FEM (Finite Element Model) is developed for simulation study, and a practical circuit is built for system verification and performance evaluation. A method of canceling the rotational torque associated with the rotating magnetic field is proposed and practically verified by testing the prototype system. It has been shown that the proposed system can transfer power up to about 950W to the load at the secondary circuit with a practically achievable quality factor Qs of 5. The FEM simulation and practical results show that unlike the conventional rotating transformer solution, the added magnetic shield increases the power transfer capability by 20%. The uncompensated power receivable at the secondary is 157 VA and 198 VA for the cases without and with aluminum shield respectively.
Automatic Idiom Identification in Wiktionary

Online resources, such as Wiktionary, provide an accurate but incomplete source of idiomatic phrases. In this paper, we study the problem of automatically identifying idiomatic dictionary entries with such resources. We train an idiom classifier on a newly gathered corpus of over 60,000 Wiktionary multi-word definitions, incorporating features that model whether phrase meanings are constructed compositionally. Experiments demonstrate that the learned classifier can provide high quality idiom labels, more than doubling the number of idiomatic entries from 7,764 to 18,155 at precision levels of over 65%. These gains also translate to idiom detection in sentences, by simply using known word sense disambiguation algorithms to match phrases to their definitions. In a set of Wiktionary definition example sentences, the more complete set of idioms boosts detection recall by over 28 percentage points.
Information network or social network?: the structure of the twitter follow graph

In this paper, we provide a characterization of the topological features of the Twitter follow graph, analyzing properties such as degree distributions, connected components, shortest path lengths, clustering coefficients, and degree assortativity. For each of these properties, we compare and contrast with available data from other social networks. These analyses provide a set of authoritative statistics that the community can reference. In addition, we use these data to investigate an often-posed question: Is Twitter a social network or an information network? The "follow" relationship in Twitter is primarily about information consumption, yet many follows are built on social ties. Not surprisingly, we find that the Twitter follow graph exhibits structural characteristics of both an information network and a social network. Going beyond descriptive characterizations, we hypothesize that from an individual user's perspective, Twitter starts off more like an information network, but evolves to behave more like a social network. We provide preliminary evidence that may serve as a formal model of how a hybrid network like Twitter evolves.
An Aperture-Coupled Linear Microstrip Leaky-Wave Antenna Array with Two-Dimensional Dual-Beam Scanning Capability

This paper describes an -band 4 1 aperture-coupled series-fed electronically steerable microstrip leaky-wave antenna (LWA) array design, which has dual-beam radiation pattern and two-dimensional (2-D) beam-scanning capability. The LWA array is operated in the first higher order mode and excited by center-fed aperture coupled for dual-beam operation. The varactor-tuned phase shifters are placed between the antenna elements. The measured half-power beamwidth of the -plane and quasi-plane radiation patterns are less than 30 . By tuning the reverse dc bias of the varactor diodes, the main beam can be scanned in azimuth plane from 13 to +13 off broadside. In the elevation plane, the beam-scanning angle is close to 20 as the operating frequency tuned from 11.58–12.5 GHz. Taking into account each phase-shifter insertion loss and phase progression, the measured results compared closely with the theoretical prediction. The proposed antenna array is suitable for wireless communication and collision warning radar systems.
Design of Android type Humanoid Robot Albert HUBO

To celebrate the 100th anniversary of the announcement of the special relativity theory of Albert Einstein, KAIST HUBO team and Hanson robotics team developed android type humanoid robot Albert HUBO which may be the world's first expressive human face on a walking biped robot. The Albert HUBO adopts the techniques of the HUBO design for Albert HUBO body and the techniques of Hanson robotics for Albert HUBO's head. Its height and weight are 137 cm and 57 Kg. Albert HUBO has 66 DOFs (31 for head motions and 35 for body motions) And head part uses `fubber' materials for smooth artificial skin and 28 servo motors for face movements and 3 servo motors for neck movements are used for generating a full range of facial expressions such as laugh, sadness, angry, surprised, etc. and body is modified with HUBO(KHR-3) introduced in 2004 to join with Albert HUBO's head and 35 DC motors are embedded for imitating various human-like body motions