nils-herrmann/clean_scirepeval huggingface dataset:
The Influence of Task Interruption on Individual Decision Making: An Information Overload Perspective

Interruptions are a common aspect of the work environment of most organizations. Yet little is known about how intemptions and their characteristics, such as frequency of occurrence, influence decision-making performance of individuals. Consequently, this paper reports the results of two experiments investigating the influence of interruptions on individual decision making. Interruptions were found to improve decision-making performance on simple tasks and to lower performance on complex tasks. For complex tasks, the frequency of interruptions and the dissimilarity of content between the primary and interruption tasks was found to exacerbate this effect. The implications of these results for future research and practice are discussed.  Decision Making, Information Overload, and Interruptions.
Massive IQ Gains in 14 Nations : What IQ Tests Really Measure

Data from 14 nations reveal IQ gains ranging from 5 to 25 points in a single generation. Some of the largest gains occur on culturally reduced tests and tests of fluid intelligence. The Norwegian data show that a nation can make significant gains on a culturally reduced test while suffering losses on other tests. The Dutch data prove the existence of unknown environmental factors so potent that they account for 15 of the 20 points gained. The hypothesis that best fits the results is that IQ tests do not measure intelligence but rather a correlate with a weak causal link to intelligence. This hypothesis can also explain differential trends on various mental tests, such as the combination of IQ gains and Scholastic Aptitude Test losses in the United States.
Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications.

A method has been devised for the electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets. The method results in quantitative transfer of ribosomal proteins from gels containing urea. For sodium dodecyl sulfate gels, the original band pattern was obtained with no loss of resolution, but the transfer was not quantitative. The method allows detection of proteins by autoradiography and is simpler than conventional procedures. The immobilized proteins were detectable by immunological procedures. All additional binding capacity on the nitrocellulose was blocked with excess protein; then a specific antibody was bound and, finally, a second antibody directed against the first antibody. The second antibody was either radioactively labeled or conjugated to fluorescein or to peroxidase. The specific protein was then detected by either autoradiography, under UV light, or by the peroxidase reaction product, respectively. In the latter case, as little as 100 pg of protein was clearly detectable. It is anticipated that the procedure will be applicable to analysis of a wide variety of proteins with specific reactions or ligands.
Imputation of Missing Data Using Machine Learning Techniques

A serious problem in mining industrial data bases is that they are often incomplete, and a significant amount of data is missing, or erroneously entered. This paper explores the use of machine-learning based alternatives to standard statistical data completion (data imputation) methods, for dealing with missing data. We have approached the data completion problem using two well-known machine learning techniques. The first is an unsupervised clustering strategy which uses a Bayesian approach to cluster the data into classes. The classes so obtained are then used to predict multiple choices for the attribute of interest. The second technique involves modeling missing variables by supervised induction of a decision tree-based classifier. This predicts the most likely value for the attribute of interest. Empirical tests using extracts from industrial databases maintained by tIoneywell customers have been done in order to compare the two techniques. These tests show both approaches are useful and have advantages and disadvantages. We argue that the choice between unsupervised and supervised classification techniques hould be influenced by the motivation for solving the missing data problem, and discuss potential applications for the procedures we are developing.
Quadrature Spatial Modulation

This paper proposes a new multiple-input-multiple-output (MIMO) technique called quadrature spatial modulation (QSM). QSM enhances the overall throughput of conventional SM systems by using an extra modulation spatial dimension. The current SM technique uses only the real part of the SM constellation, and the proposed method in this paper extends this to in-phase and quadrature dimensions. It is shown that significant performance enhancements can be achieved at the expense of synchronizing the transmit antennas. Additionally, a closed-form expression for the pairwise error probability (PEP) of generic QSM system is derived and used to calculate a tight upper bound of the average bit error probability (ABEP) over Rayleigh fading channels. Moreover, a simple and general asymptotic expression is derived and analyzed. Obtained Monte Carlo simulation results corroborate the accuracy of the conducted analysis and show the significant enhancements of the proposed QSM scheme.
An optimized distributed OLAP system for big data

To solve the problems of heterogeneous data types and large amount of calculation in making decision for big data, an optimized distributed OLAP system for big data is proposed in this paper. The system provides data acquisition for different data sources, and supports two types of OLAP engines, Impala and Kylin. First of all, the architecture of the system is proposed, consisting of four modules, data acquisition, data storage, OLAP analysis and data visualization, and the specific implementation of each module is descripted in great detail. Then the optimization of the system is put forward, which is automatic metadata configuration and the cache for OLAP query. Finally, the performance test of the system is conduct to demonstrate that the efficiency of the system is significantly better than the traditional solution.
ECG Beats Classification Using Mixture of Features

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.
Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving

For the design and test of functional modules of an automated vehicle, it is essential to define interfaces. While interfaces on the perception side, like object lists, point clouds or occupancy grids, are to a certain degree settled already, they are quite vague in the consecutive steps of context modeling and in particular on the side of driving execution. The authors consider the scene as the central interface between perception and behavior planning & control. Within the behavior planning & control block, a situation is a central data container. A scenario is a common approach to substantiate test cases for functional modules and can be used to detail the functional description of a system. However, definitions of these terms are often-at best-vague or even contradictory. This paper will review these definitions and come up with a consistent definition for each term. Moreover, we present an example for the implementation of each of these interfaces.
Feasibility Investigation of Low Cost Substrate Integrated Waveguide ( SIW ) Directional Couplers

In this paper, the feasibility of Substrate Integrated Waveguide (SIW) couplers, fabricated using single-layer TACONIC RF-35 dielectric substrate is investigated. The couplers have been produced employing a standard PCB process. The choice of the TACONIC RF-35 substrate as alternative to other conventional materials is motivated by its lower cost and high dielectric constant, allowing the reduction of the device size. The coupler requirements are 90-degree phase shift between the output and the coupled ports and frequency bandwidth from about 10.5 GHz to 12.5 GHz. The design and optimization of the couplers have been performed by using the software CST Microwave Studio c ©. Eight different coupler configurations have been designed and compared. The better three couplers have been fabricated and characterized. The proposed SIW directional couplers could be integrated within more complex planar circuits or utilized as stand-alone devices, because of their compact size. They exhibit good performance and could be employed in communication applications as broadcast signal distribution and as key elements for the construction of other microwave devices and systems.
Online Reconstruction of Structural Information from Datacenter Logs

Well-run datacenter application architectures are heavily instrumented to provide detailed traces of messages and remote invocations. Reconstructing user sessions, call graphs, transaction trees, and other structural information from these messages, a process known as sessionization, is the foundation for a variety of diagnostic, profiling, and monitoring tasks essential to the operation of the datacenter.
 We present the design and implementation of a system which processes log streams at gigabits per second and reconstructs user sessions comprising millions of transactions per second in real time with modest compute resources, while dealing with clock skew, message loss, and other real-world phenomena that make such a task challenging. Our system is based on the Timely Dataflow framework for low latency, data-parallel computation, and we demonstrate its utility with a number of use-cases and traces from a large, operational, mission-critical enterprise data center.
CRISPR provides acquired resistance against viruses in prokaryotes.

Clustered regularly interspaced short palindromic repeats (CRISPR) are a distinctive feature of the genomes of most Bacteria and Archaea and are thought to be involved in resistance to bacteriophages. We found that, after viral challenge, bacteria integrated new spacers derived from phage genomic sequences. Removal or addition of particular spacers modified the phage-resistance phenotype of the cell. Thus, CRISPR, together with associated cas genes, provided resistance against phages, and resistance specificity is determined by spacer-phage sequence similarity.
A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection

In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the Levenberg-Marquardt method is proposed to improve the learning accuracy of neural networks. Third, a feature selection method based on the conditional mutual information is developed to select a compact set of input variables for the forecasting model. Fourth, to realize an accurate ensemble forecast, partial least squares regression is utilized as a combining approach to aggregate the individual forecasts. Numerical testing shows that proposed method can obtain better forecasting results in comparison with other standard and state-of-the-art methods.
TMG: A MATLAB Toolbox for Generating Term-Document Matrices from Text Collections

A wide range of computational kernels in data mining and information retrieval from text collections involve techniques from linear algebra. These kernels typically operate on data that is presented in the form of large sparse term-document matrices (tdm). We present TMG, a research and teaching toolbox for the generation of sparse tdm's from text collections and for the incremental modification of these tdm's by means of additions or deletions. The toolbox is written entirely in MATLAB, a popular problem solving environment that is powerful in computational linear algebra, in order to streamline document preprocessing and pro-totyping of algorithms for information retrieval. Several design issues that concern the use of MATLAB sparse infrastructure and data structures are addressed. We illustrate the use of the tool in numerical explorations of the effect of stemming and different term-weighting policies on the performance of querying and clustering tasks.
A Hybrid Radar-Camera Sensing System With Phase Compensation for Random Body Movement Cancellation in Doppler Vital Sign Detection

This paper presents a Doppler radar vital sign detection system with random body movement cancellation (RBMC) technique based on adaptive phase compensation. An ordinary camera was integrated with the system to measure the subject's random body movement (RBM) that is fed back as phase information to the radar system for RBMC. The linearity of the radar system, which is strictly related to the circuit saturation problem in noncontact vital sign detection, has been thoroughly analyzed and discussed. It shows that larger body movement does not necessarily mean larger radar baseband output. High gain configuration at baseband is required for acceptable SNR in noncontact vital sign detection. The phase compensation at radar RF front-end helps to relieve the high-gain baseband from potential saturation in the presence of large body movement. A simple video processing algorithm was presented to extract the RBM without using any marker. Both theoretical analysis and simulation have been carried out to validate the linearity analysis and the proposed RBMC technique. Two experiments were carried out in the lab environment. One is the phase compensation at RF front end to extract a phantom motion in the presence of another large shaker motion, and the other one is to measure the subject person breathing normally but randomly moving his body back and forth. The experimental results show that the proposed radar system is effective to relieve the linearity burden of the baseband circuit and help compensate the RBM.
A survey on statistical methods for health care fraud detection.

Fraud and abuse have led to significant additional expense in the health care system of the United States. This paper aims to provide a comprehensive survey of the statistical methods applied to health care fraud detection, with focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted, discussing the key steps in data preprocessing, as well as summarizing, categorizing, and comparing statistical fraud detection methods. Based on this survey, some discussion is provided about what has been lacking or under-addressed in the existing research, with the purpose of pinpointing some future research directions.
A Dual-Broadband MIMO Antenna System for GSM/UMTS/LTE and WLAN Handsets

A novel dual-broadband multiple-input-multiple-output (MIMO) antenna system is developed. The MIMO antenna system consists of two dual-broadband antenna elements, each of which comprises two opened loops: an outer loop and an inner loop. The opened outer loop acts as a half-wave dipole and is excited by electromagnetic coupling from the inner loop, leading to a broadband performance for the lower band. The opened inner loop serves as two monopoles. A combination of the two monopoles and the higher modes from the outer loop results in a broadband performance for the upper band. The bandwidths (return loss >;10 dB) achieved for the dual-broadband antenna element are 1.5-2.8 GHz (~ 60%) for the lower band and 4.7-8.5 GHz (~ 58\%) for the upper band. Two U-shaped slots are introduced to reduce the coupling between the two dual-broadband antenna elements. The isolation achieved is higher than 15 dB in the lower band and 20 dB in the upper band, leading to an envelope correlation coefficient of less than 0.01. The dual-broadband MIMO antenna system has a compact volume of 50×17×0.8 mm3, suitable for GSM/UMTS/LTE and WLAN communication handsets.
A Proposal of a Password Manager Satisfying Security and Usability by Using the Secret Sharing and a Personal Server

Password managers protect users' passwords by using a master password or a security token. The security of them using the master password is weakened if users use weak master passwords. The usability of them using the security token is low since users always need the token to log in. In this paper, we propose a new framework of password managers which has a high security and a high usability by employing the secret sharing and a personal servers for a user.
Designing Social Interfaces - Principles, Patterns, and Practices for Improving the User Experience

Find loads of the designing social interfaces principles patterns and practices for improving the user experience book catalogues in this site as the choice of you visiting this page. You can also join to the website book library that will show you numerous books from any types. Literature, science, politics, and many more catalogues are presented to offer you the best book to find. The book that really makes you feels satisfied. Or that's the book that will save you from your job deadline.
Energy Feed-Forward and Direct Feed-Forward Control for Solid-State Transformer

There is large voltage deviation on the dc bus of the three-stage solid-state transformer (SST) when the load suddenly changes. The feed-forward control can effectively reduce the voltage deviation and transition time. However, conventional power feed-forward scheme of SST cannot develop the feed-forward control to the full without extra current sensor. In this letter, an energy feed-forward scheme, which takes the energy changes of inductors into consideration, is proposed for the dual active bridge (DAB) controller. A direct feed-forward scheme, which directly passes the power of DAB converter to the rectifier stage, is proposed for the rectifier controller. They can further improve the dynamic performances of the two dc bus voltages, respectively. The experimental results in a 2-kW SST prototype are provided to verify the proposed feed-forward schemes and show the superior performances.
From Backup to Hot Standby: High Availability for HDFS

Cluster-based distributed file systems generally have a single master to service clients and manage the namespace. Although simple and efficient, that design compromises availability, because the failure of the master takes the entire system down. Before version 2.0.0-alpha, the Hadoop Distributed File System (HDFS) -- an open-source storage, widely used by applications that operate over large datasets, such as MapReduce, and for which an uptime of 24x7 is becoming essential -- was an example of such systems. Given that scenario, this paper proposes a hot standby for the master of HDFS achieved by (i) extending the master's state replication performed by its check pointer helper, the Backup Node, and by (ii) introducing an automatic fail over mechanism. The step (i) took advantage of the message duplication technique developed by other high availability solution for HDFS named Avatar Nodes. The step (ii) employed another Hadoop software: ZooKeeper, a distributed coordination service. That approach resulted in small code changes, 1373 lines, not requiring external components to the Hadoop project. Thus, easing the maintenance and deployment of the file system. Compared to HDFS 0.21, tests showed that both in loads dominated by metadata operations or I/O operations, the reduction of data throughput is no more than 15% on average, and the time to switch the hot standby to active is less than 100 ms. Those results demonstrate the applicability of our solution to real systems. We also present related work on high availability for other file systems and HDFS, including the official solution, recently included in HDFS 2.0.0-alpha.
Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers

Identifying and extracting figures and tables along with their captions from scholarly articles is important both as a way of providing tools for article summarization, and as part of larger systems that seek to gain deeper, semantic understanding of these articles. While many “off-the-shelf” tools exist that can extract embedded images from these documents, e.g. PDFBox, Poppler, etc., these tools are unable to extract tables, captions, and figures composed of vector graphics. Our proposed approach analyzes the structure of individual pages of a document by detecting chunks of body text, and locates the areas wherein figures or tables could reside by reasoning about the empty regions within that text. This method can extract a wide variety of figures because it does not make strong assumptions about the format of the figures embedded in the document, as long as they can be differentiated from the main article’s text. Our algorithm also demonstrates a caption-to-figure matching component that is effective even in cases where individual captions are adjacent to multiple figures. Our contribution also includes methods for leveraging particular consistency and formatting assumptions to identify titles, body text and captions within each article. We introduce a new dataset of 150 computer science papers along with ground truth labels for the locations of the figures, tables and captions within them. Our algorithm achieves 96% precision at 92% recall when tested against this dataset, surpassing previous state of the art. We release our dataset, code, and evaluation scripts on our project website for enabling future
Trajectory clustering in road network environment

This paper proposes a new trajectory clustering scheme for objects moving on road networks. A trajectory on road networks can be defined as a sequence of road segments a moving object has passed by. We first propose a similarity measurement scheme that judges the degree of similarity by considering the total length of matched road segments. Then, we propose a new clustering algorithm based on such similarity measurement criteria by modifying and adjusting the FastMap and hierarchical clustering schemes. To evaluate the performance of the proposed clustering scheme, we also develop a trajectory generator considering the fact that most objects tend to move from the starting point to the destination point along their shortest path. The performance result shows that our scheme has the accuracy of over 95%.