LLukas22/scidocs huggingface dataset:
Passenger Segmentation Using Smart Card Data

Transit passenger market segmentation enables transit operators to target different classes of transit users for targeted surveys and various operational and strategic planning improvements. However, the existing market segmentation studies in the literature have been generally done using passenger surveys, which have various limitations. The smart card (SC) data from an automated fare collection system facilitate the understanding of the multiday travel pattern of transit passengers and can be used to segment them into identifiable types of similar behaviors and needs. This paper proposes a comprehensive methodology for passenger segmentation solely using SC data. After reconstructing the travel itineraries from SC transactions, this paper adopts the density-based spatial clustering of application with noise (DBSCAN) algorithm to mine the travel pattern of each SC user. An a priori market segmentation approach then segments transit passengers into four identifiable types. The methodology proposed in this paper assists transit operators to understand their passengers and provides them oriented information and services.
Detecting Social Engineering

Hoeschele, Michael David. M.S., Purdue University, May, 2006, Detecting Social Engineering. Major Professor: Marcus K. Rogers. This study consisted of creating and evaluating a proof of concept model of the Social Engineering Defense Architecture (SEDA) as theoretically proposed by Hoeschele and Rogers (2005). The SEDA is a potential solution to the problem of Social Engineering (SE) attacks perpetrated over the phone lines. The proof of concept model implemented some simple attack detection processes and the database to store all gathered information. The model was tested by generating benign telephone conversations in addition to conversations that include Social Engineering (SE) attacks. The conversations were then processed by the model to determine its accuracy to detect attacks. The model was able to detect all attacks and to store all of the correct data in the database, resulting in 100% accuracy.
The cellular neurobiology of depression

Major depressive disorders, long considered to be of neurochemical origin, have recently been associated with impairments in signaling pathways that regulate neuroplasticity and cell survival. Agents designed to directly target molecules in these pathways may hold promise as new therapeutics for depression.
A Systematic Review of Data Mining Approaches to Credit Card Fraud Detection

Credit card fraud is a serious and ever-growing problem with billions of dollars lost every year due to fraudulent transactions. Fraud has always been present and will always be. It is also ever changing, as the technology and usage patterns change over time, which makes CCFD (credit card fraud detection) a particularly hard problem. Traditionally, fraud detection relied solely on domain experts’ detection rules, but in the past decade or two, such solutions are being augmented with data mining models for fraud detection. The progress in this area is impeded both by the sensitive nature of the data and great commercial potential – the industrial solutions are understandably kept secret and authentic datasets are rare and few. In this paper we study the CCFD problem with its typical problems and state of the art solution. We survey the recent literature and bring a structured overview of relevant fraud detection features and data mining approaches to this problem. Key-Words: credit card, fraud detection, machine learning, systematic review
“Alexa, Can I Trust You?”

Several recent incidents highlight significant security and privacy risks associated with intelligent virtual assistants (IVAs). Better diagnostic testing of IVA ecosystems can reveal such vulnerabilities and lead to more trustworthy systems.
Smarandache Sequences: Explorations and Discoveries with a Computer Algebra System ∗

We study Smarandache sequences of numbers, and related problems, via a Computer Algebra System. Solutions are discovered, and some conjectures presented.
A review report on low power VLSI systems analysis and modeling techniques

In the present trend of CMOS IC technology, Low Power Design is a major issue in designing of a system. In order to achieve that criterion, power consumption should be minimized. In this paper, techniques which are available for reduction of power consumption at different abstraction levels are discussed in detail. With this paper, Designer can easily choose the proper methodology with the appropriate optimization technique to achieve the required goal.
Trust games: A meta-analysis

We collect data from 162 replications of the Berg, Dickhaut, and McCabe Investment game (the ‘‘trust’’ game) involving more than 23,000 participants. We conduct a meta-analysis of these games in order to identify the effect of experimental protocols and geographic variation on this popular behavioral measure of trust and trustworthiness. Our findings indicate that the amount sent in the game is significantly affected by whether payment is random, and whether play is with a simulated counterpart. Trustworthiness is significantly affected by the amount by which the experimenter multiplies the amount sent, whether subjects play both roles in the experiment, and whether the subjects are students. We find robust evidence that subjects send less in trust games conducted in Africa than those in North America. 2011 Elsevier B.V. All rights reserved.
Latin American Psychology and Social Commitment.

Originally published in Contemporary Psychology: APA Review of Books, 1996, Vol 41(5), 468-469. Three of the main ideas of liberation psychology are presented in “Writings for a Liberation Psychology” (see record 1994-98883-000): a new horizon, that psychology should stop worrying about its scientific status and serve the needs of the majority of the population; a new epistemology, to critically revise psychology from the perspective of the popular majorities; and a new praxis, to acquire new psychological knowledge it is not enough to place ourselves in the perspective of the people, it is also necessary to involve ourselves in a new praxis, an activity of transforming reality. (PsycINFO Database Record (c) 2006 APA, all rights reserved)
Underwater Video Mosaics as Visual Navigation Maps

This paper presents a set of algorithms for the creation of underwater mosaics and illustrates their use as visual maps for underwater vehicle navigation. First, we describe the automatic creation of video mosaics, which deals with the problem of image motion estimation in a robust and automatic way. The motion estimation is based on a initial matching of corresponding areas over pairs of images, followed by the use of a robust matching technique, which can cope with a high percentage of incorrect matches. Several motion models, established under the projective geometry framework, allow for the creation of high quality mosaics where no assumptions are made about the camera motion. Several tests were run on underwater image sequences, testifying to the good performance of the implemented matching and registration methods. Next, we deal with the issue of determining the 3D position and orientation of a vehicle from new views of a previously created mosaic. The problem of pose estimation is tackled, using the available information on the camera intrinsic parameters. This information ranges from the full knowledge to the case where they are estimated using a self-calibration technique based on the analysis of an image sequence captured under pure rotation. The performance of the 3D positioning algorithms is evaluated using images for which accurate ground truth is available. c © 2000 Academic Press
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services

Presently, a very large number of public and private data sets are available from local governments. In most cases, they are not semantically interoperable and a huge human effort would be needed to create integrated ontologies and knowledge base for smart city. Smart City ontology is not yet standardized, and a lot of research work is needed to identify models that can easily support the data reconciliation, the management of the complexity, to allow the data reasoning. In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed. The system allows managing a big data volume of data coming from a variety of sources considering both static and dynamic data. These data are mapped to a smart-city ontology, called KM4City (Knowledge Model for City), and stored into an RDF-Store where they are available for applications via SPARQL queries to provide new services to the users via specific applications of public administration and enterprises. The paper presents the process adopted to produce the ontology and the big data architecture for the knowledge base feeding on the basis of open and private data, and the mechanisms adopted for the data verification, reconciliation and validation. Some examples about the possible usage of the coherent big data knowledge base produced are also offered and are accessible from the RDF-store and related services. The article also presented the work performed about reconciliation algorithms and their comparative assessment and selection. & 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
A review of adaptive population sizing schemes in genetic algorithms

This paper reviews the topic of population sizing in genetic algorithms. It starts by revisiting theoretical models which rely on a facetwise decomposition of genetic algorithms, and then moves on to various self-adjusting population sizing schemes that have been proposed in the literature. The paper ends with recommendations for those who design and compare adaptive population sizing schemes for genetic algorithms.
Nonlinear Processing of a Shift Invariant DWT for Noise Reduction

A novel approach for noise reduction is presented. Similar to Donoho, we employ thresholding in some wavelet transform domain but use a nondecimated and consequently redundant wavelet transform instead of the usual orthogonal one. Another diierence is the shift invariance as opposed to the traditional orthogonal wavelet transform. We show that this new approach can be interpreted as a repeated application of Donoho's original method. The main feature is, however, a dramatically improved noise reduction compared to Donoho's approach, both in terms of the l 2 error and visually, for a large class of signals. This is shown by theoretical and experimental results, including synthetic aperture radar (SAR) images.
BMI at 4 years of age is associated with previous and current protein intake and with paternal BMI

Objectives:To evaluate possible associations between body mass index (BMI) at 4 years of age, current and previous dietary intakes and parental BMI.Methods:A follow-up of dietary intake and anthropometry in 127 4-year-old children corresponding to 54% of children who completed an initial intervention study at 18 months of age.Results:Fourteen percent of the girls and 13% of the boys were overweight (age-adjusted BMI⩾25) and 2% of the girls and 3% of the boys were obese (age-adjusted BMI⩾30). Thirty-four percent and 9% of the fathers and 19 and 7% of the mothers were overweight and obese, respectively. BMI at 6–18 months was a strong predictor of BMI at 4 years. Univariate regression analyses revealed that intake of protein in particular, and also of total energy and carbohydrates at 17/18 months and at 4 years, was positively associated with BMI at 4 years. Although BMI at 6–18 months was the strongest predictor of BMI at 4 years, in the final multivariate models of the child's BMI, protein intake at 17–18 months and at 4 years, energy intake at 4 years and the father's—but not the mother's—BMI were also independent contributing factors.Conclusions:Among these healthy children, BMI at 4 years of age tracked from 6 to 18 months of age and were associated with previous and current protein intake as well as parental BMI, particularly that of the father.
Change in sensory functioning predicts change in cognitive functioning: results from a 6-year follow-up in the maastricht aging study.

OBJECTIVES
To examine the longitudinal relationship between sensory functioning and a broad range of cognitive functions after 6 years follow-up and whether cataract surgery or first-time hearing aid use affected cognition.


DESIGN
Hierarchical regression procedures were employed to determine whether sensory functioning was predictive of cognitive performance.


SETTING
Maastricht University and the University Hospital Maastricht, the Netherlands.


PARTICIPANTS
Older Dutch adults (>/=55) enrolled in the Maastricht Aging Study (N=418).


MEASUREMENTS
Visual and auditory acuity, the Visual Verbal Learning Test (VVLT), the Stroop Color Word Test (SCWT), the Concept Shifting Task (CST), the Verbal Fluency Test, and the Letter-Digit Substitution Test (LDST).


RESULTS
A change in visual acuity was associated with change in most cognitive measures, including the total and recall scores of the VVLT, the mean score of the first two SCWT cards, the mean score of the first two CST cards and the LDST. In addition, a change in auditory acuity predicted change in memory performance (VVLT total and recall scores), and auditory acuity measured at baseline predicted change in the mean score of the first two SCWT cards and the LDST.


CONCLUSION
The findings support the notion of a strong connection between sensory acuity in auditory and visual domains and cognitive performance measures, both from a cross-sectional and a longitudinal perspective. They also suggest that it is essential to screen older individuals in a clinical context for sensory functioning so that changes in visual or auditory acuity are not interpreted as changes in cognitive performance.
Web-based quiz-game-like formative assessment: Development and evaluation

This research aims to develop a multiple-choice Web-based quiz-game-like formative assessment system, named GAM-WATA. The unique design of ‘Ask-Hint Strategy’ turns the Web-based formative assessment into an online quiz game. ‘Ask-Hint Strategy’ is composed of ‘Prune Strategy’ and ‘Call-in Strategy’. ‘Prune Strategy’ removes one incorrect option and turns the original 4-option item into a 3-option one. ‘Call-in Strategy’ provides the rate at which other test takers choose each option when answering a question. This research also compares the effectiveness of three different types of formative assessment in an e-Learning environment: paper-and-pencil test (PPT), normal Web-based test (NWBT) and GAM-WATA. In total, 165 fifth grade elementary students (from six classes) in central Taiwan participated in this research. The six classes of students were then divided into three groups and each group was randomly assigned one type of formative assessment. Overall results indicate that different types of formative assessment have significant impacts on e-Learning effectiveness and that the e-Learning effectiveness of the students in the GAM-WATA group appears to be better. Students in the GAM-WATA group more actively participate in Web-based formative assessment to do self-assessment than students in the N-WBT group. The effectiveness of formative assessment will not be significantly improved only by replacing the paper-and-pencil test with Web-based test. The strategies included in GAMWATA are recommended to be taken into consideration when researchers design Web-based formative assessment systems in the future. 2007 Elsevier Ltd. All rights reserved.
Sequential network construction for time series prediction

This paper introduces an application of the Sequential Network Construction ( snc) method to select the size of several popular neural network predictor architectures for various benchmark training sets. The specific architectures considered are a fir network and the partially recurrent Elman network and its extension, with context units also added for the output layer. We consider an enhancement of a fir network in which only those weights having relevant time delays are utilized. Bias-variance trade-off in relation to the prediction risk estimation by means of Nonlinear Cross Validation ( ncv) is discussed. The presented approach is applied to the Wölfer sunspot number data and a Mackey-Glass chaotic time series. Results show that the best predictions for the Wölfer data are computed using a fir neural network while for Mackey-Glass data an Elman network yields superior results.
Application of distance measuring with Matlab/Simulink

This paper presents the implementation in the Matlab/Simulink environment of an application for measuring distances using a video camera. Some of the advantages of using image processing as a method of measurement and of the Matlab for designing de application. The principles that where use to obtain de distance calculated where presented. Also the steps of the application implementation in Simulink where described.
The Strategic Pursuit of Moral Credentials

Abstract Moral credentials establish one's virtue and license one to act in morally disreputable ways with impunity (Monin & Miller, 2001). We propose that when people anticipate doing something morally dubious, they strategically attempt to earn moral credentials. Participants who expected to do something that could appear racist (decline to hire a Black job candidate in Studies 1 and 2, or take a test that might reveal implicit racial bias in Study 3) subsequently sought to establish non-racist credentials (by expressing greater racial sensitivity in Studies 1 and 2, or by exaggerating how favorably they perceived a Black job candidate in Study 3). Consistent with prior research, a follow-up study revealed that the opportunity to establish such credentials subsequently licensed participants to express more favorable attitudes towards a White versus a Black individual. We argue that strategically pursuing moral credentials allows individuals to manage attributions about their morally dubious behavior.
A New Approach to Improving Multilingual Summarization Using a Genetic Algorithm

Automated summarization methods can be defined as “language-independent,” if they are not based on any languagespecific knowledge. Such methods can be used for multilingual summarization defined by Mani (2001) as “processing several languages, with summary in the same language as input.” In this paper, we introduce MUSE, a languageindependent approach for extractive summarization based on the linear optimization of several sentence ranking measures using a genetic algorithm. We tested our methodology on two languages—English and Hebrew—and evaluated its performance with ROUGE-1 Recall vs. stateof-the-art extractive summarization approaches. Our results show that MUSE performs better than the best known multilingual approach (TextRank 1) in both languages. Moreover, our experimental results on a bilingual (English and Hebrew) document collection suggest that MUSE does not need to be retrained on each language and the same model can be used across at least two different languages.
Analysis of approximate nearest neighbor searching with clustered point sets

Nearest neighbor searching is a fundamental computational problem. A set of n data points is given in real d-dimensional space, and the problem is to preprocess these points into a data structure, so that given a query point, the nearest data point to the query point can be reported efficiently. Because data sets can be quite large, we are primarily interested in data structures that use only O(dn) storage. A popular class of data structures for nearest neighbor searching is the kd-tree and variants based on hierarchically decomposing space into rectangular cells. An important question in the construction of such data structures is the choice of a splitting method, which determines the dimension and splitting plane to be used at each stage of the decomposition. This choice of splitting method can have a significant influence on the efficiency of the data structure. This is especially true when data and query points are clustered in low dimensional subspaces. This is because clustering can lead to subdivisions in which cells have very high aspect ratios. We compare the well-known optimized kd-tree splitting method against two alternative splitting methods. The first, called the sliding-midpoint method, which attempts to balance the goals of producing subdivision cells of bounded aspect ratio, while not producing any empty cells. The second, called the minimum-ambiguity method is a query-based approach. In addition to the data points, it is also given a training set of query points for preprocessing. It employs a simple greedy algorithm to select the splitting plane that minimizes the average amount of ambiguity in the choice of the nearest neighbor for the training points. We provide an empirical analysis comparing these two methods against the optimized kd-tree construction for a number of synthetically generated data and query sets. We demonstrate that for clustered data and query sets, these algorithms can provide significant improvements over the standard kd-tree construction for approximate nearest neighbor searching. 1991 Mathematics Subject Classification. 68P10, 68W40.
Cervicovaginal Bacteriology and Antibiotic Sensitivity Patterns among Women with Premature Rupture of Membranes in Mulago Hospital, Kampala, Uganda: A Cross-Sectional Study

Background. A 2013 Cochrane review concluded that the choice of antibiotics for prophylaxis in PROM is not clear. In Uganda, a combination of oral erythromycin and amoxicillin is the 1st line for prophylaxis against ascending infection. Our aim was to establish the current cervicovaginal bacteriology and antibiotic sensitivity patterns. Methods. Liquor was collected aseptically from the endocervical canal and pool in the posterior fornix of the vagina using a pipette. Aerobic cultures were performed on blood, chocolate, and MacConkey agar and incubated at 35-37°C for 24-48 hrs. Enrichment media were utilized to culture for GBS and facultative anaerobes. Isolates were identified using colonial morphology, gram staining, and biochemical analysis. Sensitivity testing was performed via Kirby-Bauer disk diffusion and dilution method. Pearson's chi-squared (χ2) test and the paired t-test were applied, at a P value of 0.05. Results. Thirty percent of the cultures were positive and over 90% were aerobic microorganisms. Resistance to erythromycin, ampicillin, cotrimoxazole, and ceftriaxone was 44%, 95%, 96%, and 24%, respectively. Rupture of membranes (>12 hrs), late preterm, and term PROM were associated with more positive cultures. Conclusion. The spectrum of bacteria associated with PROM has not changed, but resistance to erythromycin and ampicillin has increased.