nils-herrmann/clean_scirepeval huggingface dataset:
Automated Structural and Spatial Comprehension of Data Tables

Title of Dissertation: AUTOMATED STRUCTURAL AND SPATIAL  Marco David Adelfio, Doctor of Philosophy, 2015 Dissertation directed by: Professor Hanan Samet Department of Computer Science Data tables on the Web hold large quantities of information, but are difficult to search, browse, and merge using existing systems. This dissertation presents a collection of techniques for extracting, processing, and querying tables that contain geographic data, by harnessing the coherence of table structures for retrieval tasks. Data tables, including spreadsheets, HTML tables, and those found in rich document formats, are the standard way of communicating structured data for typical computer users. Notably, geographic tables (i.e., those containing names of locations) constitute a large fraction of publicly-available data tables and are ripe for exposure to Internet users who are increasingly comfortable interacting with geographic data using web-based maps. Of particular interest is the creation of a large repository of geographic data tables that would enable novel queries such as “find vacation itineraries geographically similar to mine” for use in trip planning or “find demographic datasets that cover regions X, Y, and Z” for sociological research. In support of these goals, this dissertation identifies several methods for using the structure and context of data tables to improve the interpretation of the contents, even in the presence of ambiguity. First, a method for identifying functional components of data tables is presented, capitalizing on techniques for sequence labeling that are used in natural language processing. Next, a novel automated method for converting place references to physical latitude/longitude values, a process known as geotagging, is applied to tables with high accuracy. A classification procedure for identifying a specific class of geographic table, the travel itinerary, is also described, which borrows inspiration from optimization techniques for the traveling salesman problem (TSP). Finally, methods for querying spatially similar tables are introduced and several mechanisms for visualizing and interacting with the extracted geographic data are explored. AUTOMATED STRUCTURAL AND SPATIAL
Privacy-preserving electronic health record linkage using pseudonym identifiers

Accurate and reliable information sharing is essential in the healthcare domain. Currently, however, information about individual patients is held in isolated medical records maintained by numerous separate healthcare providers. Accurately linking this information is necessary for planned nationwide Electronic Health Record systems, but this must be done in a way that not only satisfies traditional data confidentiality requirements, but also meets patientspsila personal privacy needs. Here we present an architecture for linking electronic medical records in a way that gives patients control over what information is revealed about them. This is done through the use of indirect pseudonym identifiers. We then explain how this architecture can be implemented using existing technologies. A case study is used to show how our architecture satisfies data accuracy needs and patientspsila privacy requirements.
Enhancing the nutritional and health value of beef lipids and their relationship with meat quality.

This paper focuses on dietary approaches to control intramuscular fat deposition to increase beneficial omega-3 polyunsaturated fatty acids (PUFA) and conjugated linoleic acid content and reduce saturated fatty acids in beef. Beef lipid trans-fatty acids are considered, along with relationships between lipids in beef and colour shelf-life and sensory attributes. Ruminal lipolysis and biohydrogenation limit the ability to improve beef lipids. Feeding omega-3 rich forage increases linolenic acid and long-chain PUFA in beef lipids, an effect increased by ruminally-protecting lipids, but consequently may alter flavour characteristics and shelf-life. Antioxidants, particularly α-tocopherol, stabilise high concentrations of muscle PUFA. Currently, the concentration of long-chain omega-3 PUFA in beef from cattle fed non-ruminally-protected lipids falls below the limit considered by some authorities to be labelled a source of omega-3 PUFA. The mechanisms regulating fatty acid isomer distribution in bovine tissues remain unclear. Further enhancement of beef lipids requires greater understanding of ruminal biohydrogenation.
A 1.6 Gb/s, 3 mW CMOS receiver for optical communication

A 1.6 Gb/s receiver for optical communication has been designed and fabricated in a 0.25-/spl mu/m CMOS process. This receiver has no transimpedance amplifier and uses the parasitic capacitor of the flip-chip bonded photodetector as an integrating element and resolves the data with a double-sampling technique. A simple feedback loop adjusts a bias current to the average optical signal, which essentially "AC couples" the input. The resulting receiver resolves an 11 /spl mu/A input, dissipates 3 mW of power, occupies 80 /spl mu/m/spl times/50 /spl mu/m of area and operates at over 1.6 Gb/s.
Graph embedding as a new approach for unknown malware detection

Malware is any type of computer program which is developed to harm computers, networks, and information. Noticeable growth of malware development has made computer and network security a significant and challenging area in recent years. There is an intensive competition between malwares and antiviruses. Malware authors make every effort to develop new harmful codes using various programming tricks and exploits which are unseen for detection techniques. On the other hand, antivirus developers upgrade their methods and algorithms to recognize unknown malware. Therefore, an accurate and rapid detection method is an irrefutable demand in computer security area. This paper proposes a new malware detection method based on the OpCodes within an executable file. Proposed method generates a graph of operational codes (OpCode) within an executable file and then embeds this graph into eigenspace using “Power Iteration” method. This will help us represent an executable file as a linear combination of eigenvectors proportionate to their eigenvalues, which is beneficial to train machine learning classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The main advantages of our proposed method are high detection rate despite utilizing simple classifiers like KNN, acceptable computational complexity even in large scale datasets against rival methods, and low false positive rate.
FPGA implementation of an exact dot product and its application in variable-precision floating-point arithmetic

The current paper explores the capability and flexibility of field programmable gate-arrays (FPGAs) to implement variable-precision floating-point (VP) arithmetic. First, the VP exact dot product algorithm, which uses exact fixed-point operations to obtain an exact result, is presented. A VP multiplication and accumulation unit (VPMAC) on FPGA is then proposed. In the proposed design, the parallel multipliers generate the partial products of mantissa multiplication in parallel, which is the most time-consuming part in the VP multiplication and accumulation operation. This method fully utilizes DSP performance on FPGAs to enhance the performance of the VPMAC unit. Several other schemes, such as two-level RAM bank, carry-save accumulation, and partial summation, are used to achieve high frequency and pipeline throughput in the product accumulation stage. The typical algorithms in Basic Linear Algorithm Subprograms (i.e., vector dot product, general matrix vector product, and general matrix multiply product), LU decomposition, and Modified Gram–Schmidt QR decomposition, are used to evaluate the performance of the VPMAC unit. Two schemes, called the VPMAC coprocessor and matrix accelerator, are presented to implement these applications. Finally, prototypes of the VPMAC unit and the matrix accelerator based on the VPMAC unit are created on a Xilinx XC6VLX760 FPGA chip. Compared with a parallel software implementation based on OpenMP running on an Intel Xeon Quad-core E5620 CPU, the VPMAC coprocessor, equipped with one VPMAC unit, achieves a maximum acceleration factor of 18X. Moreover, the matrix accelerator, which mainly consists of a linear array of eight processing elements, achieves 12X–65X better performance.
Identifying Expertise to Extract the Wisdom of Crowds

Statistical aggregation is often used to combine multiple opinions within a group. Such aggregates outperform individuals, including experts, in various prediction and estimation tasks. This result is attributed to the "wisdom of crowds". We seek to improve the quality of such aggregates by identifying and eliminating poor performing individuals from the crowd. We propose a new measure of contribution to assess the judges' performance relative to the group and use positive contributors to build a weighting model for aggregating forecasts.
Optimal FemtoCell Density for Maximizing Throughput in 5G Heterogeneous Networks under Outage Constraints

Heterogeneous networks (HetNets), which involve densely deployed femtocells underlaid traditional macrocell network, is a promising solution to the extremely high data rate requirements of the future 5G communications. In this paper, we analyze the closed-form optimal deployment of femtocells in HetNets to maximize the network throughput under the outage constraints from both macrocells and femtocells. Specifically, we model the random distribution of macro cell users (MUEs) and femtocell base stations (FBSs) as Poisson Point Processes (PPPs). Then, the closed form expressions for outage probabilities in both uplink and downlink transmissions are derived. Further, we study the network throughput maximization problem under the outage probability constraints. Finally, With the help of convex optimization, the interval of FBS density, which contains the maximum network throughput is obtained in closed form. Simulation results validate the impact of the system parameters on the different optimal FBS density as well as the influence of interference to the maximum network throughput.
DEVELOPMENT OF AN AUGMENTED EXOSKELETON

This paper describes the conceptual design of an arm (right or left) powered exoframe (exoskeleton) which can be used in rehabilitation or by an army soldier who are debilitated to move their hands freely, and to lift the weight .This machine is designed for the application of teleoperation, virtual reality, military and rehabilitation. The option is put forward for a mechanical structure kinematical equivalent to the structure of the human arm. The elbow joint rotation is about -90 to 70 degrees. This arm can be used in both hands. This is a wearable robot i.e. mechatronic system with Velcro straps along with that it is a light weight device. It will also work mechanically with a push of a button as well as electrically with the help of solenoidal valve. Here the energy conversion is done using Pneumatic Cylinder (double acting) which is given the flow of compressed air through Solenoidal Valve, which control direction of flow and movement of piston.
Ultrawide-band coplanar waveguide-fed rectangular slot antenna

An ultrawide-band coplanar waveguide (CPW) fed slot antenna is presented. A rectangular slot antenna is excited by a 50-/spl Omega/ CPW with a U-shaped tuning stub. The impedance bandwidth, from both measurement and simulation, is about 110% (S11<-10 dB). The antenna radiates bi-directionally. The radiation patterns obtained from simulations are found to be stable across the matching band and experimental verification is provided at the high end of the band.
A Current-Sourced LED Driver Compatible With Fluorescent Lamp Ballasts

A light-emitting diode (LED) driver compatible with fluorescent lamp (FL) ballasts is presented for a lamp-only replacement without rewiring the existing lamp fixture. Ballasts have a common function to regulate the lamp current, despite widely different circuit topologies. In this paper, magnetic and electronic ballasts are modeled as nonideal current sources and a current-sourced boost converter, which is derived from the duality, is adopted for the power conversion from ballasts. A rectifier circuit with capacitor filaments is proposed to interface the converter with the four-wire output of the ballast. A digital controller emulates the high-voltage discharge of the FL and operates adaptively with various ballasts. A prototype 20-W LED driver for retrofitting T8 36-W FL is evaluated with both magnetic and electronic ballasts. In addition to wide compatibility, accurate regulation of the LED current within 0.6% error and high driver efficiency over 89.7% are obtained.
Bijective spherical parametrization with low distortion

Computing a bijective spherical parametrization of a genus-0 surface with low distortion is a fundamental task in geometric modeling and processing. Current methods for spherical parametrization cannot, in general, control the worst case distortion of all triangles nor guarantee bijectivity. Given an initial bijective spherical parametrization, with high distortion, we develop a non-linear constrained optimization problem to refine it, with objective penalizing the presence of triangles degeneration and maximal distortion. By using a dynamic adjusting parameter and a constrained, iterative inexact block coordinate descent optimization method, we efficiently and robustly achieve a bijective and low distortion parametrization with an optimal sphere radius. Compared to the state-of-the-art methods, our method is robust to initial parametrization and not sensitive to parameter choice. We demonstrate that our method produces excellent results on numerous models undergoing simple to complex shapes, in comparison to several state-of-the-art methods. & 2016
Automatic acknowledgement indexing: expanding the semantics of contribution in the CiteSeer digital library

Acknowledgements in research publications, like citations, indicate influential contributions to scientific work; however, large-scale acknowledgement analyses have traditionally been impractical due to the high cost of manual information extraction. In this paper we describe a mixture method for automatically mining acknowledgements from research documents using a combination of a Support Vector Machine and regular expressions. The algorithm has been implemented as a plug-in to the CiteSeer Digital Library and the extraction results have been integrated with the traditional metadata and citation index of the CiteSeer system. As a demonstration, we use CiteSeer's autonomous citation indexing (ACI) feature to measure the relative impact of acknowledged entities, and present the top twenty acknowledged entities within the archive.
UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition

Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, 94.05%, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes.
H-CNN: Spatial Hashing Based CNN for 3D Shape Analysis

We present a novel spatial hashing based data structure to facilitate 3D shape analysis using convolutional neural networks (CNNs). Our method builds hierarchical hash tables for an input model under different resolutions that leverage the sparse occupancy of 3D shape boundary. Based on this data structure, we design two efficient GPU algorithms namely hash2col and col2hash so that the CNN operations like convolution and pooling can be efficiently parallelized. The perfect spatial hashing is employed as our spatial hashing scheme, which is not only free of hash collision but also nearly minimal so that our data structure is almost of the same size as the raw input. Compared with existing 3D CNN methods, our data structure significantly reduces the memory footprint during the CNN training. As the input geometry features are more compactly packed, CNN operations also run faster with our data structure. The experiment shows that, under the same network structure, our method yields comparable or better benchmark results compared with the state-of-the-art while it has only one-third memory consumption when under high resolutions (i.e. 256 3).
DDSC : A Density Differentiated Spatial Clustering Technique

Finding clusters with widely differing sizes, shapes and densities in presence of noise and outliers is a challenging job. The DBSCAN is a versatile clustering algorithm that can find clusters with differing sizes and shapes in databases containing noise and outliers. But it cannot find clusters based on difference in densities. We extend the DBSCAN algorithm so that it can also detect clusters that differ in densities. Local densities within a cluster are reasonably homogeneous. Adjacent regions are separated into different clusters if there is significant change in densities. Thus the algorithm attempts to find density based natural clusters that may not be separated by any sparse region. Computational complexity of the algorithm is O(n log n).
Origami: A Convolutional Network Accelerator

Today advanced computer vision (CV) systems of ever increasing complexity are being deployed in a growing number of application scenarios with strong real-time and power constraints. Current trends in CV clearly show a rise of neural network-based algorithms, which have recently broken many object detection and localization records. These approaches are very flexible and can be used to tackle many different challenges by only changing their parameters. In this paper, we present the first convolutional network accelerator which is scalable to network sizes that are currently only handled by workstation GPUs, but remains within the power envelope of embedded systems. The architecture has been implemented on 3.09 mm2 core area in UMC 65 nm technology, capable of a throughput of 274 GOp/s at 369 GOp/s/W with an external memory bandwidth of just 525 MB/s full-duplex " a decrease of more than 90% from previous work.
Evolution of 7SK RNA and its protein partners in metazoa.

7SK RNA is a key player in the regulation of polymerase II transcription. 7SK RNA was considered as a highly conserved vertebrate innovation. The discovery of poorly conserved homologs in several insects and lophotrochozoans, however, implies a much earlier evolutionary origin. The mechanism of 7SK function requires interaction with the proteins HEXIM and La-related protein 7. Here, we present a comprehensive computational analysis of these two proteins in metazoa, and we extend the collection of 7SK RNAs by several additional candidates. In particular, we describe 7SK homologs in Caenorhabditis species. Furthermore, we derive an improved secondary structure model of 7SK RNA, which shows that the structure is quite well-conserved across animal phyla despite the extreme divergence at sequence level.
Data Mining for Web Personalization

In this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and preprocessing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly for Web personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.
Providing Effective Real-time Feedback in Simulation-based Surgical Training

Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not received much attention is the provision of automated real-time performance feedback to support the learning process. Performance feedback is actionable advice that improves novice behaviour. In simulation, automated feedback is typically extracted from prediction models trained using data mining techniques. Existing techniques suffer from either low effectiveness or low efficiency resulting in their inability to be used in real-time. In this paper, we propose a random forest based method that finds a balance between effectiveness and efficiency. Experimental results in a temporal bone surgery simulation show that the proposed method is able to extract highly effective feedback at a high level of efficiency.
A pilot study evaluating the perceptions of Certified Registered Nurse Anesthetists toward human patient simulation.

An evaluation was conducted of perceptions of practicing Certified Registered Nurse Anesthetists (CRNAs) toward use of simulation for initial certification, continuing education, and recertification. The hypothesis was that a 1-day simulation experience would improve their perceptions toward simulation use. The first phase developed content-valid questions to examine CRNAs' perceptions toward simulation. The second phase used these questions to survey practicing CRNAs in the District of Columbia, Maryland, and Virginia. During the third phase, 9 CRNAs were selected to complete a 1-day simulation experience and complete the perception questionnaire before and after the experience. Through content validity index calculations, 25 of 27 questions were retained for use in this study. A total of 378 CRNAs responded to the questionnaire. There was consensus that human patient simulation (HPS) is an important part of anesthesia provider training, and 85.7% strongly agreed, agreed, or somewhat agreed that HPS should be a required component for initial certification. Additionally, 52.9% of respondents agreed (somewhat agreed or strongly agreed) that continuing education units from HPS should be required for recertification. After the simulation, a significant (P < .05) positive change in level of agreement was noted for 13 of 25 questions, including questions related to initial training and recertification.