Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of k-Nearest-Neighbor Decision Rule in Vowel Recognition

The k-nearest-neighbor decision rule is known to provide a useful nonparametric procedure for pattern classification. This rule is applied here to a vowel recognition problem and the effect of the number (k) of nearest neighbors, the size of the trained set and the type of the distance measure on vowel recognition performance is studied. It is shown that the vowel recognition performance remains approximately constant for all the values of k. The recognition performance initially improves with the size of the training set and then converges to an asymptotic value. Selection of a better distance measure leads to a significant improvement in vowel recognition performance.
Family Care of Alzheimer's Patients: The Role of Gender and Generational Relationship on Caregiver Outcomes

In this study of 262 individuals caring for victims of Alzheimer's disease, the time-invariant factors of caregiver gender and generational relationship were analyzed for their negative impacts on caregiving. Caregiving impacts included strain in family relationships, restrictions in social activity, and decline in health. Results indicate that the impacts of caregiving are not distributed uniformly among caregivers but vary according to both caregiver gender and the generational relationship between the caregiver and the care recipient. Caregiver gender influences strain in family relationships and decline in caregiver health; generational relationship affects restrictions in caregiver social activity. To a lesser extent, caregiving involvement is affected by both gender and generational relationship.
Cognitive Processing and Narrative Discourse Production in Children with ADHD

Children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) have cognitive processing difficulties due to their disinhibition and attention deficits, which influence their scholastic performance. Cognitive processing also impacts on the production of oral narratives, an essential skill required for academic success. Therefore the relationship between cognitive processing and oral narratives is investigated. Thirty males, aged 9–11 years, were selected from English medium remedial schools and were assessed on the Cognitive Assessment System (CAS). The results were correlated to their performance on two narrative tasks, involving a Picture Sequence and a Personal Narrative. Measures of Cohesion and Coherence were analysed quantitatively. Participants' low Planning and Attention scores in this study confirmed the validity of the CAS as a diagnostic device for ADHD but were not significantly related to their oral narrative production. However, their approach to the task indicated insufficient use of planning. The structured task (picture sequence) yielded more complex stories than the unstructured task (personal narrative), which may be reflective of the participants' attention deficits. The findings suggest that narrative measures are useful instruments for oral language evaluation in children with ADHD. In addition, the importance of understanding oral narratives within a therapeutic situation is important for both therapist and child.
Analysis of brain white matter via fiber tract modeling

White matter fiber bundles of the human brain form a spatial pattern defined by the anatomical and functional architecture. Tractography applied to the tensor field in diffusion tensor imaging (DTI) results in sets of streamlines which can be associated with major fiber tracts. Comparison of fiber tract properties across subjects needs comparison at corresponding anatomical locations. Moreover, clinical analysis studying fiber tract disruption and integrity requires analysis along tracts and within cross-sections, which is hard to accomplish by conventional region of interest and voxel-based analysis. We propose a new framework for MR DTI analysis that includes tractography, fiber clustering, alignment via local shape parametrization and diffusion analysis across and along tracts. Feasibility is shown with the uncinate fasciculus and the cortico-spinal tracts. The extended set of features including fiber tract geometry and diffusion properties might lead to an improved understanding of diffusion properties and its association to normal/abnormal brain development.
Coordinated MIMO transmission for a cellular downlink

In this paper, we investigate coordinated multiple-input multiple-output (MIMO) transmission in an interference-limited cellular downlink. Different from previous work, we consider a cellular system serving more than one user using non-orthogonal channels per cell. We propose a novel coordinated transmission scheme based on the minimum mean square error (MMSE) criterion. The proposed scheme is applicable to multiple user cells. Also, efficient user selection methods along with coordinated transmission are investigated to further enhance the sum rate. Computer simulations demonstrate the effectiveness of the proposed coordination scheme and user selection methods.
Effects of Frame Rate on Two‐Dimensional Speckle Tracking–Derived Measurements of Myocardial Deformation in Premature Infants

Frame rate (FR) of image acquisition is an important determinant of the reliability of 2‐dimensional speckle tracking echocardiography (2DSTE)‐derived myocardial strain. Premature infants have relatively high heart rates (HR). The aim was to analyze the effects of varying FR on the reproducibility of 2DSTE‐derived right ventricle (RV) and left ventricle (LV) longitudinal strain (LS) and strain rate (LSR) in premature infants.
Detection and Analysis of Hidden Activities in Social Networks

Social networks are flourishing because of fast growing Internet and the World Wide Web, and more research efforts have been put on Social Network Analysis (SNA). A social network can be modeled like a graph, where the nodes represent persons, and an edge between them represent direct relationship between the persons. One of the issues in SNA is to identifying criminals from groups of individuals. In a real social network, there must have various relationships between individuals, like friendships, business relationships, and common interest relationships etc. The internet itself is a huge social network. To model such a network, link analysis need to be proposed. A page in web may treat as a node, and hyperlink between them can be represented as relationships. After social network graph is constructed, link analysis and graph partitioning algorithms may be applied to identify the hidden links in that network. Most of the existing algorithms related to social network analysis assume that their existing only one single social network, with relatively multiple relationship like Web page linkage. In typical social networks, there always have various kinds of relations. Every relation can be identified as a relation network. These different types of relations play different tasks in different roles. The work here attempts to find the problem of mining hidden relationships on social networks. Social network analysis (SNA) is a set of powerful techniques that can be used to identify clusters, patterns and hidden structures within social networks. Here the problem is identified with the following steps. 1. Analyzing information flow through the network using affected dataset, 2. Discovering non-obvious relations between actors, and 3. Identifying nodes that are directly or indirectly connected to most other nodes in the social network. This is done with the help of mining algorithms like Min-cut and Regression.
Layered Augmenting Path Algorithms

Augmenting path algorithms, first introduced by Ford and Fulkerson Ford, L. R. , D. R. Fulkerson. 1962. Flows in Networks. Princeton University Press, Princeton, N.J., are widely used in optimization. Examples include Schonsleben's polymatroid intersection algorithm Schonsleben, P. 1980. Ganzzahlige polymatroid-intersection-algorithmen. Ph.D. thesis, Eidgenossischen Technischen Hochschule, Zurich. , the maximum polymatroidal network flow algorithm of Lawler and Martel Lawler, E. L. , C. U. Martel. 1982. Computing maximal polymatroidal network flows. Math. Oper. Res.7 334--347. , Frank's algorithm for the Edmonds--Giles polyhedron Frank, A. 1984. Finding feasible vectors of Edmonds-Giles polyhedra. J. Combin. Theory Ser. B36 221-239. and Cunningham's algorithm for testing membership in matroid polyhedra Cunningham, H. W. 1984. Testing membership in matroid polyhedra. J. Combin. Theory Ser. B36 161--188. Here we give an order of magnitude improvement for the above algorithms by using an approach analogous to that of Dinits' maximum flow algorithm Dinits, E. A. 1970. Algorithm for solution of a problem of maximum flow in a network with power estimation. Soviet Math. Dokl.11 1277--1280.
Residential Photovoltaic Energy Systems in California: The Effect on Home Sales Prices

Relatively little research exists estimating the marginal impacts of photovoltaic (PV) energy systems on home sale prices. Using a large dataset of California homes that sold from 2000 through mid-2009, we find strong evidence, despite a variety of robustness checks, that existing homes with PV systems sold for a premium over comparable homes without PV systems, implying a near full return on investment. Premiums for new homes are found to be considerably lower than those for existing homes, implying, potentially, a tradeoff between price and sales velocity. The results have significant implications for homeowners, builders, appraisers, lenders, and policymakers.
Cluster of Enterobacter cloacae pseudobacteremias associated with use of an agar slant blood culturing system

From 1 February through 12 October 1990, 27 blood cultures processed at Shiprock Hospital were positive for Enterobacter cloacae; only 3 had been reported in the preceding 12 months. Twenty (74%) of the cultures were obtained from patients without clinical evidence of gram-negative septicemia. The increase in E. cloacae-positive blood cultures was temporally associated with the introduction of a new blood culturing system. To evaluate potential risk factors for an E. cloacae-positive blood culture (case-culture), we conducted a case-control study. Case-cultures were compared with 81 randomly selected cultures that were processed during the epidemic period and that were not positive for E. cloacae (controls). Because several factors suggested the possibility of pseudoinfection, we limited our analysis to the 20 blood cultures that appeared to be contaminants. Blood samples received in the laboratory during the midnight shift (5 of 20 [25%] versus 5 of 81 [6%]; odds ratio, 5.1; 95% confidence intervals, 1.01 to 24.6; P = 0.02) or present in the incubator with other E. cloacae-positive samples (17 of 20 [85%] versus 29 of 81 [36%]; odds ratio, 10.2, 95% confidence interval, 2.6 to 57.3; P < 0.001) were at increased risk for contamination. During mock experiments of the procedures for processing blood samples for culture, several breaks in aseptic technique and leakage from the blood culturing system were observed. Cultures of samples obtained from several environmental sites in the laboratory and the hand washings of two laboratory technicians grew E. cloacae. Plasmid and restriction enzyme analyses of E. cloacae isolates recovered from the patients' blood cultures, the two technicians' hand washings, and environmental sites in the laboratory indicated that all had identical plasmid profiles. Our findings suggest that the breaks in aseptic technique and the environmental contamination that occurred in association with the use of the new blood culturing system resulted in contamination of the blood cultures. This outbreak highlights the importance of routine environmental cleaning, periodic quality control assessments, and adherence to aseptic practices in clinical laboratories, particularly when new methods or equipment are introduced and/or new personnel are hired.
A federated architecture approach for Internet of Things security

Internet of Things (IoT) refers to the capability to connect, communicate and remotely manage a large number of networked, automated devices via the Internet. IoT is becoming as part of daily life and aims to extend pervasive communication and networking anytime, anywhere with any device. In this context security requirements and architectures must be properly formulated, implemented in order to enforce the security policies during their life-cycle. This paper provides a survey and analysis of security in the area of IoT introducing an approach addressed to overcome the conventional security solutions and deploy a federated architecture for dynamic prevention, detection, diagnosis, isolation, and countermeasures against cyber attacks. Based on the analysis of the most common web services, the paper defines the security needs proposing a federated model to design an architecture for secure exchange of services in IoT paradigm.
Introduction: Vehicle–terrain interaction for mobile robots

Owing to the fundamental nature of all-terrain exploration, autonomous rovers are confronted with unknown environments. This is especially apparent regarding soil interactions, as the nature of the soil is typically unknown. This work aims at establishing a framework from which the rover can learn from its interaction with the terrains encountered and shows the importance of such a method. We introduce a set of rover–terrain interaction (RTI) and remote data metrics that are expressed in different subspaces. In practice, the information characterizing the terrains, obtained from remote sensors (e.g., a camera) and local sensors (e.g., an inertial measurement unit) is used to characterize the respective remote data and RTI model. In each subspace, which can be described as a feature space encompassing either a remote data measurement or an RTI, similar features are grouped to form classes, and the probability distribution function over the features is learned for each one of those classes. Subsequently, data acquired on the same terrain are used to associate the corresponding models in each subspace and to build an inference model. Based on the remote sensor data measured, the RTI model is predicted using the inference model. This process corresponds to a near-to-far approach and provides the most probable RTI metrics of the terrain lying ahead of the rover. The predicted RTI metrics are then used to plan an optimal path with respect to the RTI model and therefore influence the rover trajectory. The CRAB rover is used in this work for the implementation and testing of the approach, which we call rover–terrain interactions learned from experiments (RTILE). This article presents RTILE, describes its implementation, and concludes with results from field tests that validate the approach. © 2009 Wiley Periodicals, Inc.
Attachment styles in everyday social interaction

For seven days, participants described the important interactions they had using a variant of the Rochester Interaction Record and reported their attachment style using Bartholomew's four-category system. A series of multilevel random coefficient analyses found that across all interactions securely attached participants, compared to those who were insecurely attached, found their interactions to be more intimate and more positive emotionally. Secure participants also felt that others were more responsive to them and their needs. Secure–insecure differences were most pronounced when secure and dismissive avoidant participants were compared. Differences between secure and fearful types were minimal. In contrast, differences in reactions to interactions with close and not close friends were more pronounced for fearful types than for secures, dismissing, or preoccupied types. These results highlight the importance of distinguishing fearful and dismissive avoidance. Copyright © 2002 John Wiley & Sons, Ltd.
The gestural joystick and the efficacy of the path tortuosity metric for human/robot interaction

This paper describes the design and evaluation of the "gestural joystick," a wearable 2-D pointing controller for mobile robots in hazardous environments that uses hand gestures. Hazardous environments, such as that of a collapsed building search, require operators to wear a significant amount of protective clothing. This protective clothing, which may include hard hats, suits, gloves, goggles, etc., reduces comfort, mobility, dexterity, load capacity, and ability to interact with conventional computer input devices. The gestural joystick, which is embedded in protective clothing, mitigates some of these impacts, but at the cost of lesser familiarity for the user and, therefore, potentially lesser performance. Effective performance metrics are required to evaluate this interface mechanism. Path tortuosity has been proposed as a performance metric for the evaluation of teleoperation of a robot, but has not been proven to be distinct from time-to-complete metrics. By injecting controlled uncertainty between the user and robot, we show, for the first time, that path tortuosity is a useful and distinct metric for the evaluation of robot teleoperation.
A comparative analysis of EDI integration in US and Japanese automobile suppliers

This paper examines EDI (Electronic Data Interchange) integration in US and Japanese automobile suppliers. The paper constructs several hypotheses based on our literature survey, and tests these hypotheses in 76 US suppliers and 93 Japanese suppliers. Our study found that: Japanese firms were more likely to make relation-specific investments into their suppliers than the US firms were; Japanese firms were likely to receive parts from their suppliers more frequently than the US firms were; Japanese firms were more likely to schedule their production right before their actual production than the US firms were; US firms were more likely to emphasise expanding their transactional bases in using EDI than Japanese firms were; US firms were more likely to integrate their customers by EDI, while Japanese firms were more likely to integrate their suppliers by EDI; EDI integration had a positive impact on EDI effectiveness in the USA, while there was no such relationship between EDI integration and EDI effectiveness in Japan.
Cognitive Performance in Centenarians and the Oldest Old: Norms from the Georgia Centenarian Study

ABSTRACT We present normative data from a large population-based sample of centenarians for several brief, global neurocognitive tasks amenable for frail elders. Comparative data from octogenarians are included. A total of 244 centenarians and 80 octogenarians from Phase III of the Georgia Centenarian Study were administered the Mini-Mental Status Examination, Severe Impairment Battery, and Behavioral Dyscontrol Scale. Centenarians (age 98–107) were stratified into three age cohorts (98–99, 100–101, 102–107), octogenarians into two 5- year cohorts (80–84, 85–89). Highly significant differences were observed between groups on all measures, with greater variation and dispersion in performance among centenarians, as well as stronger associations between age and performance. Descriptive statistics and normative ranges (unweighted and population-weighted) are provided by age cohort. Additional statistics are provided by education level. While most previous centenarian studies have used convenience samples, ours is population-based and likely more valid for comparison in applied settings. Results suggest centenarians look different than do even the oldest age range of most normative aging datasets (e.g., 85–90). Results support using global measures of neurocognition to describe cognitive status in the oldest old, and we provide normative comparisons to do so.
A feature-pair-based associative classification approach to look-alike modeling for conversion-oriented user-targeting in tail campaigns

Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been further complemented by recent work in Look-alike Modeling (LAM) which helps in creating models which are customized according to each advertiser's requirements and each campaign's characteristics, and which show ads to users who are most likely to convert on them, not just click them. In Look-alike Modeling given data about converters and nonconverters, obtained from advertisers, we would like to train models automatically for each ad campaign. Such custom models would help target more users who are similar to the set of converters the advertiser provides. The advertisers get more freedom to define their preferred sets of users which should be used as a basis to build custom targeting models. In behavioral data, the number of conversions (positive class) per campaign is very small (conversions per impression for the advertisers in our data set are much less than 10-4), giving rise to a highly skewed training dataset, which has most records pertaining to the negative class. Campaigns with very few conversions are called as tail campaigns, and those with many conversions are called head campaigns. Creation of Look-alike Models for tail campaigns is very challenging and tricky using popular classifiers like Linear SVM and GBDT, because of the very few number of positive class examples such campaigns contain. In this paper, we present an Associative Classification (AC) approach to LAM for tail campaigns. Pairs of features are used to derive rules to build a Rule-based Associative Classifier, with the rules being sorted by frequency-weighted log-likelihood ratio (F-LLR). The top k rules, sorted by F-LLR, are then applied to any test record to score it. Individual features can also form rules by themselves, though the number of such rules in the top k rules and the whole rule-set is very small. Our algorithm is based on Hadoop, and is thus very efficient in terms of speed.
Don’t You Know What Causes That? Advice, Celebration, and Justification in a Large Families Bulletin Board

A thematic analysis of an on‐line bulletin board for parents of large families revealed three prominent patterns of talk related to advice seeking and giving, celebration of family, and justification of family size. This combination of discursive behaviors seem to be largely social supportive in nature and may have implications regarding the lack of face‐to‐face social support for large families and the potential importance of communication within on‐line groups for providing that support.