Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparing Social Work Students for Interprofessional Team Practice in Health-Care Settings  

Purpose: This study presents the results of a workforce development initiative focused on preparing master’s-level social work students to work in interprofessional settings and integrated care. The study examines both trainees’ changes in interprofessional skills and attitudes as well as if there were differences in trainee experiences across race and gender. Method: Ninety-nine trainees participated in a training program and completed pre- and posttest measures on attitudes toward interdisciplinary teams and team skills. The team used multivariate analysis of variance (MANOVA) to examine the change in means and interaction effects. Results: Findings suggest that the training helps trainees acquire interdisciplinary team skills. However, results on attitudes toward interdisciplinary teams varied based on race and gender of the trainees. Discussion: Results suggest that there continue to be disparities in the training experiences of social work trainees around race and gender. Future research needs to continue to focus on this issue.
Optimum Binary Search Trees on the Hierarchical Memory Model

The Hierarchical Memory Model (HMM) of computation is similar to the standard Random Access Machine (RAM) model except that the HMM has a non-uniform memory organized in a hierarchy of levels numbered 1 through h. The cost of accessing a memory location increases with the level number, and accesses to memory locations belonging to the same level cost the same. Formally, the cost of a single access to the memory location at address a is given by m(a), where m: N -> N is the memory cost function, and the h distinct values of m model the different levels of the memory hierarchy. We study the problem of constructing and storing a binary search tree (BST) of minimum cost, over a set of keys, with probabilities for successful and unsuccessful searches, on the HMM with an arbitrary number of memory levels, and for the special case h=2. While the problem of constructing optimum binary search trees has been well studied for the standard RAM model, the additional parameter m for the HMM increases the combinatorial complexity of the problem. We present two dynamic programming algorithms to construct optimum BSTs bottom-up. These algorithms run efficiently under some natural assumptions about the memory hierarchy. We also give an efficient algorithm to construct a BST that is close to optimum, by modifying a well-known linear-time approximation algorithm for the RAM model. We conjecture that the problem of constructing an optimum BST for the HMM with an arbitrary memory cost function m is NP-complete.
A Local-Concentration-Based Feature Extraction Approach for Spam Filtering

Inspired from the biological immune system, we propose a local concentration (LC)-based feature extraction approach for anti-spam. The LC approach is considered to be able to effectively extract position-correlated information from messages by transforming each area of a message to a corresponding LC feature. Two implementation strategies of the LC approach are designed using a fixed-length sliding window and a variable-length sliding window. To incorporate the LC approach into the whole process of spam filtering, a generic LC model is designed. In the LC model, two types of detector sets are at first generated by using term selection methods and a well-defined tendency threshold. Then a sliding window is adopted to divide the message into individual areas. After segmentation of the message, the concentration of detectors is calculated and taken as the feature for each local area. Finally, all the features of local areas are combined as a feature vector of the message. To evaluate the proposed LC model, several experiments are conducted on five benchmark corpora using the cross-validation method. It is shown that the LC approach cooperates well with three term selection methods, which endows it with flexible applicability in the real world. Compared to the global-concentration-based approach and the prevalent bag-of-words approach, the LC approach has better performance in terms of both accuracy and F1 measure. It is also demonstrated that the LC approach is robust against messages with variable message length.
User Interface Personalization in News Apps

News is increasingly being accessed on smartphones and tablets, establishing mobile news reading as one of the most popular activities on mobile devices. News reading is also a very individual activity with marked differences in the way people read and access the news, however, news apps have limited personalization. In this paper, we approach news personalization as a two-dimensional problem. We discuss news personalization in terms of 'what' content is delivered to the user and 'how' that content is consumed. We present our approach towards user interface personalization in news apps and we conclude that news content recommendation and user interface personalization should co-exist in news apps.
Impact of Orbitofrontal Lesions on Electrophysiological Signals in a Stop Signal Task

Behavioral inhibition and performance monitoring are critical cognitive functions supported by distributed neural networks including the pFC. We examined neurophysiological correlates of motor response inhibition and action monitoring in patients with focal orbitofrontal (OFC) lesions (n = 12) after resection of a primary intracranial tumor or contusion because of traumatic brain injury. Healthy participants served as controls (n = 14). Participants performed a visual stop signal task. We analyzed behavioral performance as well as event-related brain potentials and oscillations. Inhibition difficulty was adjusted individually to yield an equal amount of successful inhibitions across participants. RTs of patients and controls did not differ significantly in go trials or in failed stop trials, and no differences were observed in estimated stop signal RT. However, electrophysiological response patterns during task performance distinguished the groups. Patients with OFC lesions had enhanced P3 amplitudes to congruent condition go signals and to stop signals. In stop trials, patients had attenuated N2 and error-related negativity, but enhanced error positivity. Patients also showed enhanced and prolonged post-error beta band increases for stop errors. This effect was particularly evident in patients whose lesion extended to the subgenual cingulate cortex. In summary, although response inhibition was not impaired, the diminished stop N2 and ERN support a critical role of the OFC in action monitoring. Moreover, the increased stop P3, error positivity, and post-error beta response indicate that OFC injury affected action outcome evaluation and support the notion that the OFC is relevant for the processing of abstract reinforcers such as performing correctly in the task.
Adult attachment theory and Rorschach Inkblot Method: A systematic literature review

This critical appraisal and synthesis review explores the literature on the evidence of Rorschach Inkblot Method variables as measures of the attachment theory. We searched for publications making use of EBSCO Discovery Service (EDS) which included 41 databases of which Academic Search Premier, ScienceDirect, PsycARTICLES, and PsycINFO yielded the most results. Inclusion criteria were published studies concerning Rorschach and attachment theory where participants were older than 18 years. Non-academic literature and studies not published in English were excluded. We synthesised the evidence following the narrative approach of Petticrew and Roberts (2006). Findings suggest texture, oral responses, and Rorschach oral dependency strongly associated with attachment security, with other variables indicating weaker associations with attachment.
Lost in translation? Neuroscientific research, advocacy, and the claimed transfer benefits of musical practice

ABSTRACT In recent decades there has been a proliferation of neuromyths based on oversimplifications and over-generalisations of research findings. As part of a larger project that examines the societal impacts of the arts and arts education practices, this interdisciplinary collaborative study examines the translation of recent neuromusical research into the public domain, one of the main generators of neuromyths. We review the claimed benefits of musical engagement to other domains, and the ways that researchers discuss these claims. To accomplish this, we first provide a thematic analysis of the kinds of claims that are made in research texts. We analysed 76 review articles published between 2006 and 2016 that both concern brain imaging studies in humans and involve music. The analysis highlights the ways that researchers qualify the potential benefits of music. Second, we explore the ways that neuroscientists report their results to the public and the ways that these results are taken into public discourse. In order to do this we analysed the homepages of the researchers who published at least 2 review articles in our dataset, focussing on the media linked to their homepages. We also explored over 100 websites that mention the benefits of music and also refer to brain imaging research. These overviews allow us to show that the same criteria commonly applied in research are almost never applied in public discourse. We conclude that there is an ethical imperative for researchers to take care in presenting the research results of brain imaging studies to the larger public, and furthermore argue that there is an ethical obligation for researchers to speak out against misuse of research findings.
A Novel Approach for Car License Plate Detection Based on Vertical Edges

License plate can be used for identifying vehicle since it is unique for each vehicle. In this paper proposes a fast technique for identifying the vehicle licenseplate. Here first the input image is binarized by adaptive thresholding and then image is enhanced by unwanted-line elimination algorithm (ULEA). Then on applying VEDA vertical edges of the image is detected. Then number of possible candidate license plate region is extracted out of which original LP is detected.
A multiple deficit model of reading disability and attention-deficit/hyperactivity disorder: searching for shared cognitive deficits.

BACKGROUND This study tests a multiple cognitive deficit model of reading disability (RD), attention-deficit/hyperactivity disorder (ADHD), and their comorbidity. METHODS A structural equation model (SEM) of multiple cognitive risk factors and symptom outcome variables was constructed. The model included phonological awareness as a unique predictor of RD and response inhibition as a unique predictor of ADHD. Processing speed, naming speed, and verbal working memory were modeled as potential shared cognitive deficits. RESULTS Model fit indices from the SEM indicated satisfactory fit. Closer inspection of the path weights revealed that processing speed was the only cognitive variable with significant unique relationships to RD and ADHD dimensions, particularly inattention. Moreover, the significant correlation between reading and inattention was reduced to non-significance when processing speed was included in the model, suggesting that processing speed primarily accounted for the phenotypic correlation (or comorbidity) between reading and inattention. CONCLUSIONS This study illustrates the power of a multiple deficit approach to complex developmental disorders and psychopathologies, particularly for exploring comorbidities. The theoretical role of processing speed in the developmental pathways of RD and ADHD and directions for future research are discussed.
Mapping a multidimensional emotion in response to television commercials

Unlike previous emotional studies using functional neuroimaging that have focused on either locating discrete emotions in the brain or linking emotional response to an external behavior, this study investigated brain regions in order to validate a three‐dimensional construct – namely pleasure, arousal, and dominance (PAD) of emotion induced by marketing communication. Emotional responses to five television commercials were measured with Advertisement Self‐Assessment Manikins (AdSAM®) for PAD and with functional magnetic resonance imaging (fMRI) to identify corresponding patterns of brain activation. We found significant differences in the AdSAM scores on the pleasure and arousal rating scales among the stimuli. Using the AdSAM response as a model for the fMRI image analysis, we showed bilateral activations in the inferior frontal gyri and middle temporal gyri associated with the difference on the pleasure dimension, and activations in the right superior temporal gyrus and right middle frontal gyrus associated with the difference on the arousal dimension. These findings suggest a dimensional approach of constructing emotional changes in the brain and provide a better understanding of human behavior in response to advertising stimuli. Hum Brain Mapp, 2009. © 2008 Wiley‐Liss, Inc.
Malliavin Calculus Method for Asymptotic Expansion of Dual Control Problems

We develop a technique based on Malliavin-Bismut calculus ideas, for asymptotic expansion of dual control problems arising in connection with exponential indifference valuation of claims, and with minimisation of relative entropy, in incomplete markets. The problems involve optimisation of a functional of Brownian paths on Wiener space, with the paths perturbed by a drift involving the control. In addition there is a penalty term in which the control features quadratically. The drift perturbation is interpreted as a measure change using the Girsanov theorem, leading to a form of the integration by parts formula in which a directional derivative on Wiener space is computed. This allows for asymptotic analysis of the control problem. Applications to incomplete It\^o process markets are given, in which indifference prices are approximated in the low risk aversion limit. We also give an application to identifying the minimal entropy martingale measure as a perturbation to the minimal martingale measure in stochastic volatility models.
A survey to conceptual modeling for XML

XML has become the de facto standard for representing and interchanging information in web-based applications. And conceptual modeling approaches, such as EER and UML, have been widely applied to illustrating data structure and relationships in traditional database. However, there is no standard methodology for designing XML data at conceptual level. In this paper, we give a survey to conceptual modeling for XML in terms of the basic models they employ. Most of them are based on well-established conceptual models. Some novel conceptual models are proposed to capture the hierarchical structure of XML. A set of requirements for a good conceptual models for XML are concluded. In particular, we emphasize the two phases modeling methodology and the importance of data semantics.
A hybrid routing approach for opportunistic networks

With wireless networking technologies extending into the fabrics of our working and operating environments, proper handling of intermittent wireless connectivity and network disruptions is of significance. As the sheer number of potential opportunistic application continues to surge (i.e. wireless sensor networks, underwater sensor networks, pocket switched networks, transportation networks, and etc.), the design for an effective routing scheme that considers and accommodates the various intricate behaviors observed in an opportunistic network is of interest and remained desirable. While previous solutions use either replication or coding techniques to address the challenges in opportunistic networks, the tradeoff of these two techniques only make them ideal under certain network scenarios. In this paper, we propose a hybrid scheme, named H-EC, to deal with a wide variety of opportunistic network cases. H-EC is designed to fully combine the robustness of erasure coding based routing techniques, while preserving the performance advantages of replication techniques. We evaluate H-EC against other similar strategies in terms of delivery ratio and latency, and find that H-EC offers robustness in worst-case delay performance cases while achieving good performance in small delay performance cases. We also discuss the traffic overhead issues associated with H-EC as compared to other schemes, and present several strategies that can potentially alleviate the traffic overhead of H-EC schemes.
SINR loss and user selection in massive MU‐MISO systems with ZFBF

Separating highly correlated users can reduce the loss caused by spatial correlation (SC) in multiuser multiple‐input multiple‐output (MU‐MIMO) systems. However, few accurate analyses of the loss caused by SC have been conducted. In this study, we define signal‐to‐interference‐plus‐noise ratio (SINR) loss to characterize it in multiuser multiple‐input single‐output (MU‐MISO) systems, and use coefficient of correlation (CoC) to describe the SC between users. A formula is deduced to show the accurate relation between SINR loss and CoC. Based on this relation, we propose a user selection method that utilizes CoC to minimize the average SINR loss of users in massive MU‐MISO systems. Simulation results verify the correctness of the relation and show that the proposed user selection method is very effective at reducing the loss caused by SC in massive MU‐MISO systems.
Hash Based Algorithm for Mining Association Rules

In this paper we examine the issue of mining association rules among items in a large database of sales transactions The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a su cient number of transactions The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets rst and then identifying within this candidate set those itemsets that meet the large itemset requirement Generally this is done iteratively for each large k itemset in increasing order of k where a large k itemset is a large itemset with k items To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance To address this issue we propose an e ective hash based algorithm for the candidate set generation Explicitly the number of candidate itemsets generated by the proposed algorithm is in orders of magnitude smaller than that by previous methods thus resolving the performance bottleneck Note that the generation of smaller candidate sets enables us to e ectively trim the transaction database size at a much earlier stage of the iterations thereby reducing the computational cost for later iterations signi cantly Extensive simulation study is conducted to evaluate performance of the proposed algorithm
Development of an exploration rover platform for sample return mission

This paper introduces core technologies and development process of an exploration rover for sample return mission. The purpose of development for the rover is to participate in the Sample Return Robot Challenge: pre-learning sample to be found, searching for the target sample with autonomous navigation, and retrieving the sample to a goal location, which requires many techniques from various fields to work together. In order to suppose the rover operated at the space environment such as the Moon or the Mars, mechanical characteristics and sensor/electronic/communication systems are described in this paper.
Bridging Functional and Object-Oriented Programming

Proponents of the functional programming paradigm contend that higher-order functions combined with (parametric) polymorphism result in much more reusable code. Object-oriented (OO) programming has independently followed a parallel approach to reusability. Objects can be used instead of higher-order functions and subtype polymorphism partially substitutes parametric polymorphism. In this paper, we draw strong analogies between the object-oriented and functional programming paradigms. We show that several common OO design patterns (Visitor, Virtual Proxy, Command, Observer, and more) are closely related to functional programming patterns. Additionally, we show how better support for functional programming in OO languages can result in improvements for many design patterns (Command, Virtual Proxy, Builder, Abstract Factory, and more). The context for demonstrating our ideas is the FC++ library. FC++ adds to C++ many of the capabilities of modern functional programming languages, without any modification to the base language. Compared to other attempts to program functionally in C++, FC++ offers much richer support for polymorphic functions (allowing functions that take polymorphic functions as arguments and/ or return them as results).
Personality Basis for Partisan News Media Use: Openness to Experience and Consumption of Liberal News Media

This study explores the link between Openness to Experience (one dimension of the Big Five personality traits) and attitude-challenging news media use (crosscutting exposure). Two distinctive behavioral tendencies of Openness (political affinity toward liberal ideals vs. tolerance to political differences) allow us to propose two equally possible, but mutually exclusive, hypotheses regarding the association between Openness and crosscutting exposure. Two American National Election Study surveys conducted during the 2012 and 2016 U.S. presidential elections were used to test the competing hypotheses. Across the two elections, a positive link between Openness and liberal news media use was found regardless of self-reported party identification. Openness promoted attitude-consistent news media use (selective exposure) among Democrats, whereas it encouraged crosscutting exposure among Republicans. Our findings suggest the distinction between “selective” and “crosscutting” exposure based on one’s party identification may mask the common foundation that influences exposure decisions to partisan news media.
Retinal development and function in a ‘blind’ mole

Animals adapted to dark ecotopes may experience selective pressure for retinal reduction. No previous studies have explicitly addressed the molecular basis of retinal development in any fossorial mammal. We studied retinal development and function in the Iberian mole Talpa occidentalis, which was presumed to be blind because of its permanently closed eyes. Prenatal retina development was relatively normal, with specification of all cell types and evidence of dorsoventral regionalization. Severe developmental defects occurred after birth, subsequent to lens abnormalities. ‘Blind’ Iberian moles had rods, cones and rod nuclear ultrastructure typical of diurnal mammals. DiI staining revealed only contralateral projections through the optic chiasm. Y-maze experiments demonstrated that moles retain a photoavoidance response. Over-representation of melanopsin-positive retinal ganglion cells that mediate photoperiodicity was observed. Hence, molecular pathways of eye development in Iberian moles retain the adaptive function of rod/cone primary vision and photoperiodicity, with no evidence that moles are likely to completely lose their eyes on an evolutionary time scale.