nils-herrmann/clean_scirepeval huggingface dataset:
Author name disambiguation in MEDLINE

We recently described “Author-ity,” a model for estimating the probability that two articles in MEDLINE, sharing the same author name, were written by the same individual. Features include shared title words, journal name, coauthors, medical subject headings, language, affiliations, and author name features (middle initial, suffix, and prevalence in MEDLINE). Here we test the hypothesis that the Author-ity model will suffice to disambiguate author names for the vast majority of articles in MEDLINE.  Enhancements include: (a) incorporating first names and their variants, email addresses, and correlations between specific last names and affiliation words; (b) new methods of generating large unbiased training sets; (c) new methods for estimating the prior probability; (d) a weighted least squares algorithm for correcting transitivity violations; and (e) a maximum likelihood based agglomerative algorithm for computing clusters of articles that represent inferred author-individuals.  Pairwise comparisons were computed for all author names on all 15.3 million articles in MEDLINE (2006 baseline), that share last name and first initial, to create Author-ity 2006, a database that has each name on each article assigned to one of 6.7 million inferred author-individual clusters. Recall is estimated at ∼98.8%. Lumping (putting two different individuals into the same cluster) affects ∼0.5% of clusters, whereas splitting (assigning articles written by the same individual to >1 cluster) affects ∼2% of articles.  The Author-ity model can be applied generally to other bibliographic databases. Author name disambiguation allows information retrieval and data integration to become person-centered, not just document-centered, setting the stage for new data mining and social network tools that will facilitate the analysis of scholarly publishing and collaboration behavior.  The Author-ity 2006 database is available for nonprofit academic research, and can be freely queried via http://arrowsmith.psych.uic.edu.
Feature Space Optimization for Semantic Video Segmentation

We present an approach to long-range spatio-temporal regularization in semantic video segmentation. Temporal regularization in video is challenging because both the camera and the scene may be in motion. Thus Euclidean distance in the space-time volume is not a good proxy for correspondence. We optimize the mapping of pixels to a Euclidean feature space so as to minimize distances between corresponding points. Structured prediction is performed by a dense CRF that operates on the optimized features. Experimental results demonstrate that the presented approach increases the accuracy and temporal consistency of semantic video segmentation.
Grip force modeling of a tendon-driven prosthetic hand

This paper describes a grip force analysis of a tendon-driven prosthetic hand. The grip force is equivalent to tensile force transmitted by a link mechanism. Assuming that the tensile force to pull tendon is constant, the grip force according to the angle of MCP joint is analyzed by the statics. From experimental results, we show the maximum grip force of tendon-driven finger is 6N when the constant tensile force is 14N.
Dimensionality reduction for large-scale neural recordings

Most sensory, cognitive and motor functions depend on the interactions of many neurons. In recent years, there has been rapid development and increasing use of technologies for recording from large numbers of neurons, either sequentially or simultaneously. A key question is what scientific insight can be gained by studying a population of recorded neurons beyond studying each neuron individually. Here, we examine three important motivations for population studies: single-trial hypotheses requiring statistical power, hypotheses of population response structure and exploratory analyses of large data sets. Many recent studies have adopted dimensionality reduction to analyze these populations and to find features that are not apparent at the level of individual neurons. We describe the dimensionality reduction methods commonly applied to population activity and offer practical advice about selecting methods and interpreting their outputs. This review is intended for experimental and computational researchers who seek to understand the role dimensionality reduction has had and can have in systems neuroscience, and who seek to apply these methods to their own data.
Self-Delimiting Neural Networks

Self-delimiting (SLIM) programs are a central concept of theoretical computer science, particularly algorithmic information & probability theory, and asymptotically optimal program search (AOPS). To apply AOPS to (possibly recurrent) neural networks (NNs), I introduce SLIM NNs. A typical SLIM NN is a general parallel-sequential computer. Its neurons have threshold activation functions. Its output neu-rons may affect the environment, which may respond with new inputs. During a computational episode, activations are spreading from input neurons through the SLIM NN until the computation activates a special halt neuron. Weights of the NN's used connections define its program. Halting programs form a prefix code. An episode may never activate most neurons, and hence never even consider their outgoing connections. So we trace only neurons and connections used at least once. With such a trace, the reset of the initial NN state does not cost more than the latest program execution. This by itself may speed up traditional NN implementations. To efficiently change SLIM NN weights based on experience, any learning algorithm (LA) should ignore all unused weights. Since prefixes of SLIM programs influence their suffixes (weight changes occurring early in an episode influence which weights are considered later), SLIM NN LAs should execute weight changes online during activation spreading. This can be achieved by applying AOPS to growing SLIM NNs. Since SLIM NNs select their own task-dependent effective size (=number of used free parameters), they have a built-in way of addressing overfitting, with the potential of effectively becoming small and slim whenever this is beneficial. To efficiently teach a SLIM NN to solve many tasks, such as correctly classifying many different patterns, or solving many different robot control tasks, each connection keeps a list of tasks it is used for. The lists may be efficiently updated during training. To evaluate the overall effect of currently tested weight changes, a SLIM NN LA needs to re-test performance only on the efficiently computable union of tasks potentially affected by the current weight changes. Search spaces of many existing LAs (such as hill climbing and neuro-evolution) can be greatly reduced by obeying restrictions of SLIM NNs. Future SLIM NNs will be implemented on 3-dimensional brain-like multi-processor hardware. Their LAs will minimize task-specific total wire length of used connections, to encourage efficient solutions of subtasks by subsets of neurons that are physically close. The novel class of SLIM NN LAs is currently being probed in ongoing experiments …
Factors Influencing Adult Learners' Decision to Drop Out or Persist in Online Learning

The number of adult learners who participate in online learning has rapidly grown in the last two decades due to online learning's many advantages. In spite of the growth, the high dropout rate in online learning has been of concern to many higher education institutions and organizations. The purpose of this study was to determine whether persistent learners and dropouts are different in individual characteristics (i.e., age, gender, and educational level), external factors (i.e., family and organizational supports), and internal factors (i.e., satisfaction and relevance as sub-dimensions of motivation). Quantitative data were collected from 147 learners who had dropped out of or finished one of the online courses offered from a large Midwestern university. Dropouts and persistent learners showed statistical differences in perceptions of family and organizational support, and satisfaction and relevance. It was also shown that the theoretical framework, which includes family support, organizational support, satisfaction, and relevance in addition to individual characteristics, is able to predict learners' decision to drop out or persist. Organizational 9upport and relevance were shown to be particularly predictive. The results imply that lower dropout rates can be achieved if online program developers or instrdctors find ways to enhance the relevance of the course. It also implies thai adult learners need to be supported by their organizations in order for them to finish online courses that they register for.
Semantic interaction for visual text analytics

Visual analytics emphasizes sensemaking of large, complex datasets through interactively exploring visualizations generated by statistical models. For example, dimensionality reduction methods use various similarity metrics to visualize textual document collections in a spatial metaphor, where similarities between documents are approximately represented through their relative spatial distances to each other in a 2D layout. This metaphor is designed to mimic analysts' mental models of the document collection and support their analytic processes, such as clustering similar documents together. However, in current methods, users must interact with such visualizations using controls external to the visual metaphor, such as sliders, menus, or text fields, to directly control underlying model parameters that they do not understand and that do not relate to their analytic process occurring within the visual metaphor. In this paper, we present the opportunity for a new design space for visual analytic interaction, called semantic interaction, which seeks to enable analysts to spatially interact with such models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents. Further, we demonstrate how semantic interactions can be implemented using machine learning techniques in a visual analytic tool, called ForceSPIRE, for interactive analysis of textual data within a spatial visualization. Analysts can express their expert domain knowledge about the documents by simply moving them, which guides the underlying model to improve the overall layout, taking the user's feedback into account.
Massive MIMO Design for 5G Networks: An Overview on Alternative Antenna Configurations and Channel Model Challenges

With the growth of mobile data application and the ultimate expectations of 5G technology, the need to expand the capacity of the wireless networks is inevitable. Massive MIMO technique is currently taking a major part of the ongoing research, and expected to be the key player in the new cellular technologies. This papers presents an overview of the major aspects related to massive MIMO design including, antenna array general design, configuration, and challenges, in addition to advanced beamforming techniques and channel modeling and estimation issues affecting the implementation of such systems.
Detecting Jihadist Messages on Twitter

Jihadist groups such as ISIS are spreading online propaganda using various forms of social media such as Twitter and YouTube. One of the most common approaches to stop these groups is to suspend accounts that spread propaganda when they are discovered. This approach requires that human analysts manually read and analyze an enormous amount of information on social media. In this work we make a first attempt to automatically detect messages released by jihadist groups on Twitter. We use a machine learning approach that classifies a tweet as containing material that is supporting jihadists groups or not. Even tough our results are preliminary and more tests needs to be carried out we believe that results indicate that an automated approach to aid analysts in their work with detecting radical content on social media is a promising way forward. It should be noted that an automatic approach to detect radical content should only be used as a support tool for human analysts in their work.
Assessment of algorithms for oscillometric blood pressure measurement

Three different algorithms for finding blood pressure through the oscillometric method were researched and assessed. It is shown that these algorithms are based on two different underlying approaches. The estimated values of systolic and diastolic blood pressure are compared against the nurse readings. The best two approaches turned out to be the linear approximation algorithm and the points of rapidly increasing/decreasing slope algorithm. Future work on combining these two algorithms using algorithm fusion is envisaged.
Grid Current Regulation of a Three-Phase Voltage Source Inverter With an LCL Input Filter

Many grid connected power electronic systems, such as STATCOMs, UPFCs, and distributed generation system interfaces, use a voltage source inverter (VSI) connected to the supply network through a filter. This filter, typically a series inductance, acts to reduce the switching harmonics entering the distribution network. An alternative filter is a LCL network, which can achieve reduced levels of harmonic distortion at lower switching frequencies and with less inductance, and therefore has potential benefits for higher power applications. However, systems incorporating LCL filters require more complex control strategies and are not commonly presented in literature. This paper proposes a robust strategy for regulating the grid current entering a distribution network from a three-phase VSI system connected via a LCL filter. The strategy integrates an outer loop grid current regulator with inner capacitor current regulation to stabilize the system. A synchronous frame PI current regulation strategy is used for the outer grid current control loop. Linear analysis, simulation, and experimental results are used to verify the stability of the control algorithm across a range of operating conditions. Finally, expressions for “harmonic impedance” of the system are derived to study the effects of supply voltage distortion on the harmonic performance of the system.
Design of Prepaid Energy Meter based on PROTEUS

Prepaid Energy Meter has been implemented in several countries. In fact, the disadvantage of the system is the behavior control of the users. Recharging should be carried out on the meter. The problem arises when the consumers leave and the electric pulses discharged. Therefore, a system is needed to control the electrical pulse wherever they are. Prepaid energy meter is proposed and simulated by using PROTEUS software. The system was designed by using ATmega128 as a microcontroller. This paper evaluates the accuracy of voltage and current measurement that produced by the model. The simulations show that our proposed prepaid energy meter produces minimum error compared to the actual volt and actual ampere meter.
Classification of cotton leaf spot diseases using image processing edge detection techniques

This Proposed Work exposes, a advance computing technology that has been developed to help the farmer to take superior decision about many aspects of crop development process. Suitable evaluation and diagnosis of crop disease in the field is very critical for the increased production. Foliar is the major important fungal disease of cotton and occurs in all growing Indian regions. In this work we express new technological strategies using mobile captured symptoms of cotton leaf spot images and categorize the diseases using HPCCDD Proposed Algorithm. The classifier is being trained to achieve intelligent farming, including early Identification of diseases in the groves, selective fungicide application, etc. This proposed work is based on Image RGB feature ranging techniques used to identify the diseases (using Ranging values) in which, the captured images are processed for enhancement first. Then color image segmentation is carried out to get target regions (disease spots). Next Homogenize techniques like Sobel and Canny filter are used to Identify the edges, these extracted edge features are used in classification to identify the disease spots. Finally, pest recommendation is given to the farmers to ensure their crop and reduce the yeildloss.
Detection of UAV hijacking and malfunctions via variations in flight data statistics

Detection of potential hijackings of Unmanned Aerial Vehicles (UAVs) is an important capability to have for the safety of the future airspace and prevention of loss of life and property. In this paper, we propose using basic statistical measures as a fingerprint to flight patterns that can be checked against previous flights. We generated baseline flights and then simulated hijacking scenarios to determine the extent of the feasibility of this method. Our results indicated that all of the direct hijacking scenarios were detected, but flights with control instability caused by malicious acts were not detected.
GAIN ENHANCEMENT OF THE VIVALDI ANTENNA WITH BAND NOTCH CHARACTERISTICS USING ZERO-INDEX METAMATERIAL

In this letter, a tapered slot ultrawide band (UWB) Vivaldi antenna with enhanced gain having band notch characteristics in the WLAN/WiMAX band is presented. In this framework, a reference tapered slot Vivaldi antenna is first designed for UWB operation that is, 3.1–10.6 GHz using the standard procedure. The band-notch operation at 4.8 GHz is achieved with the help of especially designed complementary split ring resonator (CSRR) cell placed near the excitation point of the antenna. Further, the gain of the designed antenna is enhanced substantially with the help of anisotropic zero index metamaterial (AZIM) cells, which are optimized and positioned on the substrate in a particular fashion. In order to check the novelty of the design procedure, three distinct Vivaldi structures are fabricated and tested. Experimental data show quite good agreement with the simulated results. As the proposed antenna can minimize the electromagnetic interference (EMI) caused by the IEEE 802.11 WLAN/WiMAX standards, it can be used more efficiently in the UWB frequency band. VC 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:233–238, 2016; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.29534
From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians

In the machine learning community it is generally believed that graph Laplacians corresponding to a finite sample of data points converge to a continuous Laplace operator if the sample size increases. Even though this assertion serves as a justification for many Laplacianbased algorithms, so far only some aspects of this claim have been rigorously proved. In this paper we close this gap by establishing the strong pointwise consistency of a family of graph Laplacians with datadependent weights to some weighted Laplace operator. Our investigation also includes the important case where the data lies on a submanifold of R.
Support vector machines in remote sensing : A review

Awide range of methods for analysis of airborneand satellite-derived imagery continues to be proposed and assessed. In this paper, we review remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology. This review is timely due to the exponentially increasing number of works published in recent years. SVMs are particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples, a common limitation for remote sensing applications. However, they also suffer from parameter assignment issues that can significantly affect obtained results. A summary of empirical results is provided for various applications of over one hundred published works (as of April, 2010). It is our hope that this survey will provide guidelines for future applications of SVMs and possible areas of algorithm enhancement.
Leveraging Digital Technologies: How Information Quality Leads to Localized Capabilities and Customer Service Performance

The Indian banking sector has undergone rapid transformation since 1991—the year India started a series of economic reforms. As part of the reforms, along with public sector banks, private sector banks started operations in the country. Due to the rapid reforms of the banking sector and the increased competition, Indian banks are now featured prominently on the global stage. Estimates indicate that 22 Indian banks are in the list of top-1000 banks and 5 of them feature in the list of top-500 banks (Singh 2007). BANK, the bank studied by us, is one of these top banks.
Okamoto Beats Schnorr: On the Provable Security of Multi-Signatures

A multisignature scheme allows a group of signers to collaboratively sign a message, creating a single signature that convinces a verifier that every individual signer approved the message. The increased interest in technologies to decentralize trust has triggered the proposal of two highly efficient Schnorr-based multisignature schemes designed to scale up to thousands of signers, namely CoSi by Syta et al. (S&P 2016) andMuSig by Maxwell et al. (ePrint 2018). TheMuSig scheme was presented with a proof under the one-more discrete-logarithm assumption, while the provable security of CoSi has so far remained an open question. In this work, we prove that CoSi and MuSig cannot be proved secure without radically departing from currently known techniques (and point out a flaw in the proof of MuSig). We then present DG-CoSi, a double-generator variant of CoSi based on the Okamoto (multi)signature scheme, and prove it secure under the discrete-logarithm assumption in the random-oracle model. Our experiments show that the second generator in DG-CoSi barely affects scalability compared to CoSi, allowing 8192 signers to collaboratively sign a message in under 1.5 seconds, making it a highly practical and provably secure alternative for large-scale deployments.
Movie Description

Audio description (AD) provides linguistic descriptions of movies and allows visually impaired people to follow a movie along with their peers. Such descriptions are by design mainly visual and thus naturally form an interesting data source for computer vision and computational linguistics. In this work we propose a novel dataset which contains transcribed ADs, which are temporally aligned to full length movies. In addition we also collected and aligned movie scripts used in prior work and compare the two sources of descriptions. We introduce the Large Scale Movie Description Challenge (LSMDC) which contains a parallel corpus of 128,118 sentences aligned to video clips from 200 movies (around 150 h of video in total). The goal of the challenge is to automatically generate descriptions for the movie clips. First we characterize the dataset by benchmarking different approaches for generating video descriptions. Comparing ADs to scripts, we find that ADs are more visual and describe precisely what is shown rather than what should happen according to the scripts created prior to movie production. Furthermore, we present and compare the results of several teams who participated in the challenges organized in the context of two workshops at ICCV 2015 and ECCV 2016.
Bi-directional conversion between graphemes and phonemes using a joint N-gram model

We present in this paper a statistical model for languageindependent bi-directional conversion between spelling and pronunciation, based on joint grapheme/phoneme units extracted from automatically aligned data. The model is evaluated on spelling-to-pronunciation and pronunciation-tospelling conversion on the NetTalk database and the CMU dictionary. We also study the effect of including lexical stress in the pronunciation. Although a direct comparison is difficult to make, our model’s performance appears to be as good or better than that of other data-driven approaches that have been applied to the same tasks.