Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical trial cohort selection based on multi-level rule-based natural language processing system

OBJECTIVE Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients' eligibility based on their longitudinal medical records. This work was part of the 2018 National NLP Clinical Challenges (n2c2) Shared-Task and Workshop on Cohort Selection for Clinical Trials. MATERIALS AND METHODS The authors developed an integrated rule-based clinical NLP system which employs a generic rule-based framework plugged in with lexical-, syntactic- and meta-level, task-specific knowledge inputs. In addition, the authors also implemented and evaluated a general clinical NLP (cNLP) system which is built with the Unified Medical Language System and Unstructured Information Management Architecture. RESULTS AND DISCUSSION The systems were evaluated as part of the 2018 n2c2-1 challenge, and authors' rule-based system obtained an F-measure of 0.9028, ranking fourth at the challenge and had less than 1% difference from the best system. While the general cNLP system didn't achieve performance as good as the rule-based system, it did establish its own advantages and potential in extracting clinical concepts. CONCLUSION Our results indicate that a well-designed rule-based clinical NLP system is capable of achieving good performance on cohort selection even with a small training data set. In addition, the investigation of a Unified Medical Language System-based general cNLP system suggests that a hybrid system combining these 2 approaches is promising to surpass the state-of-the-art performance.
People counting in classroom based on video surveillance

Currently, the switches of the lights and other electronic devices in the classroom are mainly relied on manual control, as a result, many lights are on while no one or only few people in the classroom. It is important to change the current situation and control the electronic devices intelligently according to the number and the distribution of the students in the classroom, so as to reduce the considerable waste of electronic resources. This paper studies the problem of people counting in classroom based on video surveillance. As the camera in the classroom can not get the full shape contour information of bodies and the clear features information of faces, most of the classical algorithms such as the pedestrian detection method based on HOG (histograms of oriented gradient) feature and the face detection method based on machine learning are unable to obtain a satisfied result. A new kind of dual background updating model based on sparse and low-rank matrix decomposition is proposed in this paper, according to the fact that most of the students in the classroom are almost in stationary state and there are body movement occasionally. Firstly, combining the frame difference with the sparse and low-rank matrix decomposition to predict the moving areas, and updating the background model with different parameters according to the positional relationship between the pixels of current video frame and the predicted motion regions. Secondly, the regions of moving objects are determined based on the updated background using the background subtraction method. Finally, some operations including binarization, median filtering and morphology processing, connected component detection, etc. are performed on the regions acquired by the background subtraction, in order to induce the effects of the noise and obtain the number of people in the classroom. The experiment results show the validity of the algorithm of people counting.
Intelligent Control Strategies for the Acrobot Using Neurocontroller Optimized by Genetic Algorithm

This paper focuses on two control methods which are constructed based on neurocontroller (NC) and genetic algorithm (GA) for the Acrobot. A switching controller is first introduced where an NC optimized by GA is used for the swing-up stage and a linear quadratic regulator (LQR) is applied for stabilization. Next, we show that it is possible to handle both control stages of the Acrobot by using only the NC, called global NC, with a different evaluation function for GA. This controller seems to be the first smooth control strategy for the Acrobot. In order to analyze the characteristics and verify the effectiveness of the proposed control methods, numerical simulations are implemented using different timing constraints. A comparison with a classical controller is also provided. Simulation results show that the proposed controllers work effectively.
Learning error-correcting output codes from data

A polychotomizer which assigns the input to one of K3 classes is constructed using a set of dichotomizers which assign the input to one of two classes. Defining classes in terms of the dichotomizers is the binary decomposition matrix of size K×L where each of the K3 classes is written as error-correcting output codes (ECOC), i.e., an array of the responses of binary decisions made by L dichotomizers. We use linear dichotomizers and by combining them suitably, we build nonlinear polychotomizers, thereby reducing complex decisions into a group of simpler decisions. We propose a method to learn the error-correcting codes from data based on soft weight sharing which forces parameters to take one of a set (here two: -1/+1) values. Simulation results on eight datasets indicate that compared with a linear one-per-class polychotomizer and ECOC proper, these methods generate more accurate classifiers, using less dichotomizers than pairwise classifiers.
Improvements and Future Challenges for the Research Infrastructure in the Field 'Experimental Economics'

Experimental economics is an established method of generating controlled and replicable empirical knowledge. It is complementary to other empirical methods in the social sciences. The research infrastructure for laboratory experiments is very good in Europe and also in Germany. One useful instrument would be to develop a short socio-economic questionnaire with questions already used in surveys that experimental economists could use to administer to their participants. The analyses of the selectivity of subject pools would then be an easy task. However, among experimental economists no standard exists yet, which limits the comparability of respective data sets. An effort shall be undertaken to “create” such a common questionnaire. The status quo with regard to data reporting is that no standard has emerged yet. There exists one data repository (in the United States) where data of experiments are collected and are freely available. Building up a data archive that integrates (merges) existing data is very laborious and requires substantial scientific inputs of interested researchers.
Analysis of single flying capacitor converter by the state-space averaging technique

The single flying capacitor converter (SFCC) is a two-quadrant power supply for energy storage and transfer system. It has applications in nuclear research and pulsed power systems in which superconductive coils are used in superconductive magnets. The authors derive analytical expressions for the time-averaged behavior of the SFCC by using the state-space averaging technique. Useful expressions are found for the average behavior of the SFCC. The results can be used in the design, control, and optimum operation of the system. <<ETX>>
Multi-objective flexible job shop scheduling using hybrid differential evolution algorithm

The authors addressed multi objective flexible job shop scheduling problems using hybrid differential evolution algorithm for minimisation of makespan, total machine load and critical machine load. The differential evolution algorithm is a stochastic-based adaptive scheme used for global optimisation over continuous space and to apply it for flexible job shop scheduling problem a suitable encoding mechanism is required. In this work random keys encoding mechanism is used to generate schedules that deals with floating point vectors. A non-dominated sorting algorithm is used to find the set of non-dominated solutions for the given scheduling problem. The proposed approach is extensively tested on a set of standard flexible job shop scheduling instances reported in the literature and it is found that the proposed algorithm is performing well on all the test problems.
Dual eigenspace method for human face recognition

The authors present an effective scheme called the dual eigenspace method (DEM) for automated face recognition. Based on the K-L transform, the dual eigenspaces are constructed by extracting algebraic features of training samples and applied to face identification with a two-layer minimium distance classifier. Experimental results show that DEM is significantly better than the traditional eigenface method (TEM).
Overcoming health care disparities via better cross-cultural communication and health literacy

Health care disparities have multiple causes; the dynamics of the physician-patient encounter is one of the causes that can be modified. Here, we discuss specific recommendations related to cross-cultural communication and health literacy as practical steps to providing more equitable health care to all patients. Health care disparities have multiple causes. Poor communication is a cause that can be modified.
The effect of individually-induced processes on image-based overlay and diffraction-based overlay

In this paper, set of wafers with separated processes was prepared and overlay measurement result was compared in two methods; IBO and DBO. Based on the experimental result, theoretical approach of relationship between overlay mark deformation and overlay variation is presented. Moreover, overlay reading simulation was used in verification and prediction of overlay variation due to deformation of overlay mark caused by induced processes. Through this study, understanding of individual process effects on overlay measurement error is given. Additionally, guideline of selecting proper overlay measurement scheme for specific layer is presented.
A statistical model for microcellular multipath propagation environment

Multipath propagation characteristics are of great importance in evaluating the performance of digital systems and designing wireless links. This paper describes statistical multipath propagation modeling for microcells in indoor, pedestrian, and vehicular environments. The channel impulse response is measured to clarify the statistical characteristics in urban areas, residential areas and indoors. The measuring channel sounder is the sliding correlator using 30 Mbit/s PN code with center frequency of 2.6 GHz. The measurements clarify the features of delay profile, mean RMS (root mean square) delay spread and local (or instantaneous) RMS delay spread. Furthermore, tapped-delay-line parameters for multipath propagation simulators based on this modeling are proposed. The proposed model is applicable to the evaluation of transmission characteristics of wireless digital communication systems in multipath fading environments.
Stamp 5.0 : structural time series analyser, modeller and predictor

Part 1: installation procedure for STAMP. Part 2 Tutorials on structural time series modelling: getting started on simple univariate modelling tutorial on components tutorial on interventions and explanatory variables tutorial on multivariate models applications in macroeconomics and finance. Part 3 STAMP tutorials: the basic skills tutorial on graphics tutorial on data input and output tutorial on data transformation and description tutorial on model building and testing. Part 4 Statistical output: descriptive statistics statistical treatment of models model output output from STAMP model estimation selected model and estimation output summary statistics the sample period test hyperparameters variances and standard deviations cycle and AR(1) covariance matrices (multivariate models) factor loading matrices (multivariate models) transformed hyperparameters and standard errors final state analysis of state regression analysis seasonal tests cycle tests data in logs goodness of fit prediction error variance prediction error mean deviation coefficients of determination information criteria - AIC and BIC components series with components detrended seasonally adjusted individual seasonals data in logs joint components residuals correlogram periodogram and spectrum cumulative statistics and graphs distribution statistics heteroskedasticity. Part 5 STAMP manuals: general information STAMP files the information and binary data files (.IN7/.BN7) spreadsheet files (.XLS,.WKS,.WK1) human-readable files (.DAT) the information and ASCII data files (.IN7/.DAT) the print file (.PRN) results file (.OUT) PCX files (.PCX) algebra file (.ALG) forecast file (.STF) configuration file (STAMP.CFG) STAMP output the results window is full printscreen graphics the graphics window printing graphs graphics modes graphics display configuration limitations of STAMP memory management out of memory memory is low memory fragmentation saving memory command-line options session logging and playback lags the database size variable names missing values details of algebra STAMP menus. Part 6 Appendices: giveman file open configuration save configuration exit DataManager convert edit information file reconstruct information file compress information and binary file information video system setup colours install 800 x 600 x 16 HPP, PSP and NCDC GRAFPLUS user manual DOS extender manual STAMP error messages.
Paper-and-Pencil Programming Strategy toward Computational Thinking for Non-Majors: Design Your Solution

The paper-and-pencil programming strategy (PPS) is a way of representing an idea logically by any representation that can be created using paper and pencil. It was developed for non-computer majors to improve their understanding and use of computational thinking and increase interest in learning computer science. A total of 110 non-majors in their sophomore year were assigned to either a Logo or a PPS course with attendance being 2 hours per week for 15 weeks. To measure the effectiveness of PPS, the Group Assessment of Logical Thinking and a self-assessment survey pre- and post-test were used. Findings indicated that PPS not only improved students' overall logical thinking as much as did Logo programming learning, but also increased scores on one more subscale of logical thinking than did the Logo course. In addition, PPS significantly helped students understand the concept of computational thinking and increased their interest in learning computer science.
PKR: A Personalized Knowledge Recommendation System for Virtual Research Communities

This paper proposes a personalized explicit- and tacit-knowledge recommendation model for a virtual research community. The proposed model aims to recommend both useful journal documents (explicit knowledge) and community members who can discuss the information (tacit knowledge) on-line in real time. A middleware system, the personalized knowledge recommender (PKR) system, that was constructed from this model is presented. The model combines content-based (CB) and collaborative filtering (CF) methods, to make explicit and tacit knowledge recommendations. Unlike other similar systems, this system adapts CB and CF in different ways to provide users with not only “interest-related” documents for reference but also connections to the “related knowledge owners” for further on-line discussion. An e-journal paper recommendation for a virtual research community is used as an example to evaluate the performance of PKR in terms of mean absolute error, precision, recall and F-measure. PKR creates the specialty of explicit- and tacit-knowledge recommendations.
Dynamic approaches to thwart adversary intelligence gathering

The DARPA Information Assurance Program did initial research in the area of dynamic network defense, trying to prove that dynamic network reconfiguration would inhibit an adversary's ability to gather intelligence, and thus degrade the ability to successfully launch an attack. A technique that enabled dynamic network address translation of the IP address and TCP port number combinations in packet headers was implemented in an experimental network. Two tests were conducted: one to demonstrate that it is possible to disrupt an adversary's ability to sniff network traffic effectively, and another to show that the ability of intrusion detection tools to detect an adversary can be improved. The tests were successful.
Engineering optimization using a real-parameter genetic-algorithm-based hybrid method

A hybrid optimization algorithm which combines the respective merits of the genetic algorithm and the simulated annealing algorithm is proposed. The proposed algorithm incorporates adaptive mechanisms designed to adjust the probabilities of the cross-over and mutation operators such that its hill-climbing ability towards the optimum solution is improved. The algorithm is used to optimize the weight of four planar or space truss structures and the results are compared with those obtained using other well-known optimization schemes. The evaluation trials investigate the performance of the algorithm in optimizing over discrete sizing variables only and over both discrete sizing variables and continuous configuration variables. The results show that the proposed algorithm consistently outperforms the other optimization methods in terms of its weight-saving capabilities. It is also shown that the global searching ability and convergence speed of the proposed algorithm are significantly improved by the inclusion of adaptive mechanisms to adjust the values of the genetic operators. Hence the hybrid algorithm provides an efficient and robust technique for solving engineering design optimization problems.
Getty's Synoname™ and its cousins: A survey of applications of personal name‐matching algorithms

The study reported in this article was commissioned by the Getty Art History Information Program (AHIP) as a background investigation of personal name‐matching programs in fields other than art history, for purposes of comparing them and their approaches with AHIP's Synoname™ project. We review techniques employed in a variety of applications, including art history, bibliography, genealogy, commerce, and government, providing a framework of personal name characteristics, factors in selecting matching techniques, and types of applications. Personal names, as data elements in information systems, vary for a wide range of legitimate reasons, including cultural and historical traditions, translation and transliteration, reporting and recording variations, as well as typographical and phonetic errors. Some matching applications seek to link variants, while others seek to correct errors. The choice of matching techniques will vary in the amount of domain knowledge about the names that is incorporated, the sources of data, and the human and computing resources required. Personal name‐matching techniques may be included in name authority work, information retrieval, or duplicate detection, with some applications matching on name only, and others combining personal names with other data elements in record linkage techniques. We discuss both phonetic‐ and pattern‐matching techniques, reviewing a range of implemented and proposed name‐matching techniques in the context of these factors. © 1992 John Wiley & Sons, Inc.
A fuzzy classifier for visual crowding estimates

A trainable vision-based system is presented, which is able to perform reliable, real time estimates of the crowding level present on the platforms of underground stations. Taking as input standard the B/W images of the scene, a classification of the crowding level is performed in terms of five qualitative crowding classes, ranging from no people to overcrowding. Visual feature extraction and fuzzy classification methods employed are described in detail, as well as the procedure adopted to train the hyper basis functions neural classifier. Experiments and results obtained on real data are reported, with some emphasis on the possibility of empirically estimating the generalization capability of the proposed system.
Improving Speech Recognition Rate through Analysis Parameters

Abstract Speech signal is redundant and non-stationary by nature. Because of vocal tract inertness these variations are not very rapid and the signal can be considered as stationary in short segments. It is presumed that in short-time magnitude spectrum the most distinct information of speech is contained. This is the main reason for speech signal analysis in frame-by-frame manner. The analyzed speech signal is segmented into overlapping segments (so-called frames) for this purpose. Segments of 15-25 ms with the overlap of 10-15 ms are used usually. In this paper we present results of our investigation of analysis window length and frame shift influence on speech recognition rate. We have analyzed three different cepstral analysis approaches for this purpose: mel frequency cepstral analysis (MFCC), linear prediction cepstral analysis (LPCC) and perceptual linear prediction cepstral analysis (PLPC). The highest speech recognition rate was obtained using 10 ms length analysis window with the frame shift varying from 7.5 to 10 ms (regardless of analysis type). The highest increase of recognition rate was 2.5 %.
Correlation Based Method to Detect and Remove Redundant Web Document

The enrichment of internet has resulted in the flooding of abundant information on WWW with more replicas. As the duplicated web pages increase the indexing space and time complexity, finding and removing these pages becomes significant for search engines and other likely system which will improve on accuracy of search results as well as search speed. Web content mining plays a vital role in resolving these aspects. Existing algorithm for web content mining focus attention on applying weightage to structured documents whereas in this research work, a mathematical approach based on linear correlation is developed to detect and remove the duplicates present in both structured and unstructured web document. In the proposed work, linear correlation between two web documents is found out. If the correlated value is 1 then the documents are said to be exactly redundant and it should be eliminated otherwise not redundant.
Intensity and color descriptors for texture classification

In this paper we present a descriptor for texture classification based on the histogram of a local measure of the color contrast. The descriptor has been concatenated to several other color and intensity texture descriptors in the state of the art and has been experimented on three datasets. Results show, in nearly every case, a performance improvement with respect to results achieved by baseline methods thus demonstrating the effectiveness of the proposed texture features. The descriptor has also demonstrated to be robust with respect to global changes in lighting conditions.
Different Spaces: Staff Development for Web 2.0.

This paper reports on a collaborative staff development activity run across two Australian universities, for academic staff integrating Web 2.0 technologies into their teaching. It describes a three-week long virtual workshop on teaching with wikis, where participants in two groups developed a group project as students and then assessed the work as teachers. Participants were guided through a central Wikis in Higher Education wiki which provided the resources and communication supports. The experience suggested that teaching in a Web 2.0 space requires new thinking about pedagogy and that peer learning and the development of an online community are helpful for effective professional development. In closing, the paper reflects on the successes and limitations of this virtual workshop model.